Educator Parameter Matrix

124 parameters across 7 dimensions · 69 content pieces · 59 educators · How It Works →

Parameters 124
Content Pieces 69
Educators 59
Coverage 4257/8556
Legend:
Value present
Pending
Not applicable
High confidence
Medium
Low
Dimension:
ID Parameter Description
Grant Sanderson (3Blue1Brown) The Hairy Ball Theorem 8.2M · 2.4M views · 0.29 v/f YT · EN · 29.7 min 75/124
Adam Digital tiktok 7480911557380132114 12K views TT · EN · ? 58/124
Adam Digital tiktok 7611529334670609684 TT · EN · 0.9 min 39/124
Agentic James Instagram Reel DTTgKynEl00 IG · EN · 2.1 min 64/124
Agentic James Instagram Reel DVP2qv2jVGX IG · EN · 1.6 min 64/124
Ai Advantage ai agents automations explained 424K · 14K views · 0.03 v/f YT · EN · ? 65/124
Ai Explained Claude Co founder Claims 407K · 69K views · 0.17 v/f YT · EN · 22.2 min 75/124
Aishwarya Srinivasan how to learn llms 2026 54K · 85K views · 1.56 v/f YT · EN · ? 65/124
Alex Finn Claude Code Lessons 137K · 38K views · 0.28 v/f YT · EN · 19.9 min 75/124
Allie K Miller reel DMVi8iJPwwr IG · EN · ? 59/124
Andrej Karpathy Intro to Large Language Models 1.3M · 3.5M views · 2.70 v/f YT · EN · 59.8 min 72/124
Mitchell Moffit & Gregory Brown (Asapscience) Instagram Reel IG · EN · 1.4 min 64/124
Brand Nat tiktok 7487957152565513490 18K views TT · EN · ? 58/124
Codewithclaude tiktok 7484089037146328327 3K views TT · EN · ? 58/124
Cole Medin cole medin ? · EN · 20.0 min 77/124
Dani Buller Instagram Reel DD4qkWPgzOq IG · HE · 2.2 min 64/124
Dani Buller Instagram Reel DTky7cZCFFv IG · HE · 3.9 min 65/124
Dave Ebbelaar Context Engineering for AI Agents 245K · 20K views · 0.08 v/f YT · EN · 25.0 min 75/124
David Ondrej web scraping ai agents power 357K · 22K views · 0.06 v/f YT · EN · 23.9 min 65/124
Deeplearningai reel DG1yI2PJcPl IG · EN · ? 59/124
Doron Fishler ep171 guinea pigs ? · HE · 32.1 min 39/124
Doron Fishler Instagram Reel DM8O6YtsiMl IG · HE · 1.7 min 63/124
Doron Fishler Instagram Reel DQrFPZHjaDw (Animated) IG · HE · 2.6 min 63/124
Doron Fishler instagram IG · EN · 1.7 min 53/124
Dr. Julie Smith 4 Hidden Signs of Loneliness IG · EN · 0.5 min 62/124
Jeff Delaney (Fireship) TanStack Start in 100 Seconds 4.1M · 318K views · 0.08 v/f YT · EN · 2.3 min 64/124
Fluent In Finance reel DClDl15tB0w IG · EN · ? 17/124
Gabe Dannenbring reel DJkcVzGxP D IG · EN · ? 60/124
Greg Isenberg Claude Code Skill My Smartest Friends Us 562K · 34K views · 0.06 v/f YT · EN · 25.4 min 75/124
Harsh The Mentor tiktok 7609750361460460830 TT · EN · 0.6 min 51/124
Harsh The Mentor tiktok 7612727335351061791 2K views TT · EN · ? 59/124
Historyphotographed reel DHjpaEwtbuT IG · EN · ? 59/124
Dr. Nicole Lepera (@The.Holistic.Psychologist) Instagram Reel IG · EN · 1.1 min 66/124
Humphrey Yang Net Worth Top 10% IG · EN · 1.0 min 66/124
Intuitive Ml reel C1GKGahSQCq IG · EN · ? 17/124
James Briggs openai agents sdk tools explained 80K · 2K views · 0.03 v/f YT · EN · 18.1 min 65/124
Kane Kallaway reel DUGabFVDb0e IG · EN · ? 61/124
Kate Biberdorf (Katethechemist) Instagram Reel IG · EN · 0.7 min 51/124
Learn Machinelearning reel C4qD5u0vqxE IG · EN · ? 62/124
Clemence Arbib (Learnenglishwithclemence) Instagram Reel IG · EN · 1.3 min 65/124
Liam Ottley Automate 70% of Business w/ Claude Code 742K · 36K views · 0.05 v/f YT · EN · 16.5 min 75/124
Matt Wolfe AI Tools You'll Actually Use 910K · 173K views · 0.19 v/f YT · EN · 16.1 min 65/124
Marwa Kemicha (@Miss_Englishteacher_) Instagram Reel IG · EN · 0.4 min 65/124
Haley Sacks (Mrs. Dow Jones) Starting from Scratch in Your 30s IG · EN · 1.5 min 63/124
Nate Herk From Zero to First Agentic AI Workflow 550K · 54K views · 0.10 v/f YT · EN · 26.4 min 65/124
Neil Degrasse Tyson Contrast Interview (StarTalk) 5.4M · 733K views · 0.14 v/f YT · EN · ? 77/124
Neil Degrasse Tyson Instagram Reel (Contrast) IG · EN · ? 67/124
Networkchuck AI Prompting Secrets 5.2M · 591K views · 0.12 v/f YT · EN · 24.0 min 75/124
Jeremy Schneider (Personalfinanceclub) Instagram Reel IG · EN · 0.7 min 58/124
Jonny Thomson (Philosophyminis) Kierkegaard's Concept of Regret IG · EN · 1.2 min 63/124
Pythonlearnerr reel DHs9twTqbEw IG · EN · ? 19/124
Raven Baxter (@Raventhesciencemaven) Instagram Reel IG · EN · 0.6 min 65/124
Riley Brown (@Realrileybrown) Instagram Reel IG · EN · 1.1 min 65/124
Rourke Genhq reel DQXMI2ojNrn IG · EN · ? 59/124
Rowan Cheung satya nadella ai agents 99K · 382K views · 3.87 v/f YT · EN · ? 65/124
Sal Khan Instagram Reel CEsetPNA90Q IG · EN · 1.7 min 65/124
Sal Khan Teaching Basic Addition (Origins of Alge YT · EN · 7.3 min 79/124
Sal Khan TED Talk: Let's use video to reinvent ed 27.2M · 1.3M views · 0.05 v/f YT · EN · 15.6 min 79/124
Sam Witteveen claude skills sops for agents 117K · 48K views · 0.41 v/f YT · EN · ? 63/124
Setupsai reel DTuwjEKierQ IG · EN · ? 59/124
Skill Leap Ai claude cowork first ai real employee 319K · 109K views · 0.34 v/f YT · EN · ? 65/124
Spanish With Andrea reel DHMFBLHO5TR IG · EN · ? 19/124
Teach2Ai tiktok 7489226671648754999 38K views TT · EN · ? 59/124
Dianna Cowern (Physics Girl) Faster Than Light Galaxies IG · EN · 2.4 min 65/124
Tiffany Janzen (@Tiffintech) Instagram Reel IG · EN · 0.6 min 65/124
Tina Huang How I Learn Things Really Fast (with AI) 1.1M · 112K views · 0.10 v/f YT · EN · 24.2 min 75/124
Karoly Zsolnai Feher (Two Minute Papers) Anthropic Found Out Why AIs Go Insane 1.8M · 226K views · 0.13 v/f YT · EN · 9.2 min 64/124
Wes Roth Build ANYTHING with Oz by Warp 312K · 17K views · 0.05 v/f YT · EN · 17.5 min 75/124
Vivian Tu (Yourrichbff) Instagram Reel IG · EN · 1.5 min 65/124
D1: Explanation
E1Concept DensityHow many distinct ideas per minute12.82.35.75medium — ~6 core concepts (automation, workflow, trigger, achigh — 4 major claims from Dario Amodei essay each contain smedium — ~8 core concepts (ML fundamentals, LLMs, transformemedium-high — 8 discrete lessons, each a distinct claim or r1.61.53.64.81.52.11.82.8high — ~10 distinct concepts (context engineering, prompt en0.32.51.221.527.56.10.30.2540.5530.7210.80.41.92.321.80.60.51.401.7highlowlow0.8very_low2.521.5extreme1.2mediumvery_highhigh1.72.71.5very_high
E2Explanation Duration / ConceptHow long they spend unpacking each idea60151210.511.5long — each concept gets 3–6 min of treatment; demo segmentsmedium-long — each claim gets ~4–5 min of treatment; sub-clamedium — each step gets ~2–4 min; breadth over depth; designshort — each lesson gets ~1.5–2.5 min; opinion-first format 354012940283321medium-long — core concepts get 3–5 min each; formal definit18095507514085309.81015030501059558020827915031268034105127430691002040750243050608070928535224075
E3Directness ScoreRatio of on-point content to tangents0.60.80.90.70.8high — leads with demo output, states claim first ('this is medium-high — states each claim clearly upfront, then immedihigh — leads with audience pain point ('you're paralyzed'), very high — consistently opinion-forward ('stop listening to0.80.60.80.80.70.80.70.8medium — anti-hype framing ('biggest companies are strugglin0.80.80.70.50.30.70.90.90.40.50.80.80.30.80.80.90.60.910.40.910.80.80.60.30.70.69570.540.80.60.7100.87890.80.70.89
E4Vocabulary LevelFlesch-Kincaid grade level of language used9.69.55.27.210.15.18.55.84.495.4
E5Sentence Length (Mean)Average words per sentence20.816.420.51413.316.922.810.229.812.21321.38.510.125.623.614.219.911.8
E6Sentence Length (Std Dev)How much sentence length varies11.211.910.77.911.113.21810.457.113.211.717.711.98.137.513.18.112.97.4
E7Question FrequencyHow often they ask questions (any type)00000.301.3003.500000
E8Question Type DistributionWhat kinds of questions: rhetorical, genuine, provocative, etc.
E9Jargon DensityTechnical/domain-specific terms relative to total words2.51.42.36.87.5medium — LLM, agentic, workflow, automation are used withouthigh — scaling laws, feedback loops, compute, agentic, alignmedium — LLM, transformer, RAG, fine-tuning used but explainlow — avoids technical jargon; uses plain language ('the ext0.450.73.233.81.42very high — context engineering, LLM inference, token budget1.8300.50.508.701.21.50.501.513.220.501.51.4022.91.40.51.800.9lowlowmedium3.4none6.41.55.1none3.3low_mediumhighmedium1.23.39.7medium_high
E10Jargon Explanation RateHow many jargon terms they explain on first use85003020high — every key term gets a working definition on first uselow — most jargon assumed known; occasional inline gloss ('shigh — each technical term gets an accessible definition; usn/a — very little jargon to explain; when technical terms ap100751006030785090high — every technical term defined on first use; often uses852010010010010025100656010010001007080100100607010090829509210010.50.520n/a858560n/a550.70.90.88870450.8
E11Analogy FrequencyHow often they use comparisons to explain concepts0.10000low — ~1–2 analogies total; primarily uses concrete examplesmedium — ~3–4 analogies (law contract feedback loop, unit temedium — ~3 analogies; most prominent: house-building analoglow — ~1–2 loose comparisons; primarily uses direct examples0.20.10000.100.5low — ~1–2 analogies; relies on visual diagrams (progression0.201110000.3100110.11000.50.1020.20.220.4040000.300.130.200.10110.20.10.21
E12Analogy ConcretenessHow tangible the analogies are: abstract, concrete, or visceral5high when used — email draft example is very specific (trigghigh — analogies are concrete and domain-specific; law contrhigh — house-building analogy is mapped explicitly to each lhigh when used — comparisons grounded in personal experience4444medium when used — analogies are conceptual (prompt engineer445434343434455555n/an/an/a4n/a244n/a3n/avery_highvery_high554high
E13Repetition PatternsKey phrases or ideas repeated for emphasis000000[{"phrase": "we're going to", "count": 20}, {"phrase": "goin00dont_break_rules000[{"phrase": ">> right? >>", "count": 13}, {"phra00[{"phrase": "i don't know", "count": 6}, {"phrase": "so this[{"phrase": "to show you", "count": 4}, {"phrase": "a little0000
E14Opening StrategyHow they begin: hook, question, story, statement, cold startstorysocial_proofbold_claimpersonal_hook_with_claimresult_firstdemo-first — opens with live tool walkthrough of email automauthority + scope frame — opens with 'I read the 20,000-wordpain-point empathy + credentials — opens with audience pain credentials + promise — opens with extreme personal authoritauthority_credentialcontext_settingvisual_data_hookcuriosity_gapprovocative_challengeproblem_then_promiseorigin_storytopic_hook_with_promiseproblem framing + credentialing — opens with industry pain pdirect_hookcontext_bridgemystery_hookbig_picture_frameconfrontational_challengenumbered_list_hookdirect_topicsituational_comedy_hookintrigue_hookalarm_hookprovocative_claimdirect_addressprovocative_claimstat_hookchapter_roadmapvulnerability_hooksituational_comedydirect_demo_startimmediate_drill_startcallback_hookdirect_value_declarationscene_dropaudience_targeting_hookauthority_and_contextmundane_to_profounddisclaimer_then_reframeprovocative_challengeaccidental_pod_title_mishearparallel_paradox_hookincident_hookbold_claimmid_process_entryteaser_montage_then_credentialself_introductiondirect_topic_statementcontext_settinganalogy_hookseries_then_curiosity_gapbold_claim_hooknews_hook_plus_tldrquestion_from_audiencequestion_hookcredibility_plus_promisebold_claimpersonal_problem_then_solution_revealdirect_challenge
E15Build PatternHow explanation develops: linear, spiral, contrast, mystery, accumulationproblem_solutionstep_by_stepdemo_accumulationprocess_walkthroughprocess_walkthroughconcrete → abstract → concrete — demo first, then definitionstructured enumeration — explicit 4-claim framework stated asequential scaffolded steps — numbered learning path (Step 1episodic list — 8 independent lessons in order; no cumulativnumbered_listlinearcontrast_revealfeature_accumulationsingle_demo_proofsequential_demoproblem_solutioninvestigative_layer_revealdefinition-first progressive complexity — establishes formalsequential_problem_solutionlinear_sequentialnarrative_revealanalogy_escalationevidence_argumentparallel_listlinearvignette_streamdemo_explorationclaim_evidence_implicationevent_metrics_lessonstory_revealclaim_acknowledge_actionnumbered_listtaxonomy_then_demoprofile_then_system_then_philosophyescalating_complexityparallel_repetitionhype_vision_ctafrequency_ordered_listnarrative_progressionnumbered_steps_with_secretconcept_then_buildlayer_by_layer_socraticthesis_through_counter_escalationiterative_improvementhook_generalize_complicate_reframelinear_newsstory_then_revealdemo_revealinterview_qa_thematic_progressionlist_of_intentionschronological_narrativeproblem_solutionconcept_then_demonstrationbullet_deliveryuse_case_progression_increasing_complexitymisconception_then_positivelayered_correctioncause_effect_resolutionframework_sequentialproblem_solutionproblem_solution_build_revealnumbered_debunk
E16Key Moment DeliveryHow the main insight/revelation lands — setup + delivery techniqueThe proof reveal is delivered with deliberate slowness afterdemo_revealrapid_demo_showcasesystem_reveal_cascadespecific_output_revealstated plainly — key distinction (AI controls workflow = agecalm analytical emphasis — key claims stated without vocal dwarm but clear — key recommendations delivered with personalvocal emphasis + strong language — key opinions delivered wiaccumulated_candorThe 'LLM as just two files' reveal is delivered with casual controversy_revealembedded_demo_clipshock_revealDelivered through live demonstration moments — showing the adata_anchorembedded_knesset_clipdiagram-anchored — key conceptual moments accompanied by vislive_demo_revealdirect_imperativereveal_twistparadox_punchlinedirect_critiquefinal_item_deepestTanner's hypothetical pitch delivered as direct quote: 'Whattonal_whiplashsocial_proof_plus_demorole_reframedata_punchgratitude_peakempathy_bridgedata_plus_counterintuitive_framecode_execution_revealsystem_reveal_midrobot_reasoning_midrhythm_as_deliverydeclaration_with_superlativesCasual discovery reveals — 'This is so cool' moments while dpractical_directions_deliveredsecret_revealWorkflow completion reveals — showing the PDF competitive anpayoff_after_socratic_builduppivot_to_counter_exampleLive prompt results reveal — reading the output aloud with ereframe_punchlinestakes_revealembedded_clip_reveallive_output_revealQuotable soundbites delivered conversationally; peak phrasesself_aware_humorEtymology reveal at ~15% mark: 'al-jabr means restoration orlive_demo_with_commentarySkills composability/portability reveal at ~60%: 'you can usfeature_revealResults reveals after each use case: organized screenshot fostudent_builders_revealstat_then_pivotstay_with_meTime-savings calculation reveal — builds to '20 hours saved 'Dear fellow scholars, this is Two Minute Papers with Dr. KáAI Pulse live dashboard reveal at ~85%: 'Here is AI Pulse Limath_reveal
E17Closing StrategyHow they end: CTA, reflection, cliffhanger, callback, summaryopen_questioncta_with_benefitcta_dmteaser_with_ctageneralizable_claim_with_ctathree-step summary + sponsor + personal sign-off — 'start wiaudience question + no CTA — ends with 'which predictions docommunity invitation + personal backstory — closes with 'telenthusiasm close + newsletter CTA — ends with genuine exciteself_intro_and_ctafuture_teaserpersonal_callbackquestion_to_engageself_deprecating_solidaritysynthesis_plus_ctaproduct_cta_with_specificssystemic_call_to_actionpath-forward recommendation — closes by recommending next victa_warmupaction_directiveescalation_teaseparadox_acceptanceempowerment_invitationfinal_list_itemcall_to_actioncomedic_callbackwarm_reciprocalurgency_ctalesson_then_ctagratitude_acknowledgmentempowerment_challengecta_plus_comment_hooksummary_teaseinspirational_mindsetself_deprecating_humorimplicit_demonstration_completedrill_completionwebinar_cta_plus_subscribeauthenticity_closenatural_scene_endinevitability_close_plus_followsummary_plus_communityzoom_out_philosophicalphilosophical_reframe_no_resolutionmeta_insight_plus_personalre_take_requestopen_question_no_resolutionopen_cliffhangerwit_ambiguityexcitement_invitationvalue_vision_closewarm_humor_closeopen_ended_continuationcall_to_actioncall_to_action_with_reflectionsingle_word_summaryplatform_promo_with_teaserdirect_cta_plus_verdictdistance_revealreassurance_reframeassessment_plus_communityfuture_teaserfuture_vision_teaserthreat_plus_cta
E18Transition TechniqueHow they move between topics: bridge phrase, question, story, signpostquestionnumbered_stepslive_demo_cutssequential_connectorstemporal_connectorsexplicit verbal signposting — 'Now let me show you...', 'Jusnumbered enumeration — 'claim 1', 'claim 2', etc.; transitionumbered step labels — 'Step 1', 'Step 2', etc.; explicit annumbered lesson labels — 'Lesson 1', 'Lesson 2', etc.; explinumbered_anchorsverbal_bridgecontrast_pivotfeature_list_connectorslive_action_narrationnumbered_framework_signpostingrhetorical_question_pivotevidence_stackingsection header slides + verbal signposting — uses visual secnumbered_sequentialenumerationnarrative_flowcandidate_enumerationcounter_argumentnumbered_enumerationverbal_bridgetopic_jumpconversational_anywaylogical_escalationlogical_flowconversational_flowlogical_chainnumbered_verbal_markerslogical_connectorstopic_pivotqa_sequencerhythmic_continuationstream_of_consciousnessconnector_phrasedialogue_exchangestep_numberingstep_signpostinglayer_announcementrhetorical_question_pivotsnarrative_momentumcontrastive_pivotsconnective_logicnarrative_chronologyverbal_cue_sequentialinterviewer_bridging_questionsadditive_listingtemporal_connectorssignpostingtopical_pivots_with_now_and_all_rightnone_telegraphicnumbered_use_case_headerstopical_connectorsbuilding_on_prior_questionlogical_causationanalogy_continuationverbal_bridgephase_headers_and_food_metaphornumbered_list
E19Self-Correction FrequencyHow often they correct themselves mid-explanation
D2: Voice
V1Mean PitchAverage fundamental frequency of voice
V2Pitch MedianCentral tendency of pitch (less outlier-sensitive)113.6112.9113.2138.2107.2111.6221.9161.5221.9104.7102.9207.1114.9133.5196.6209.5161.5112.2107.2128.2125.3125.3195.4102.358.1117.692.8117.6252170.1135139.8103.561.6147.3109.7213.1117.6164.3319.3210.7118.2125.3102.6112.9130.4110.353.3216.8119.6259.4132.7124.5105.3150.7126.7123.1156.9324.9181.3200254.9188.8162.5102.3219.4
V3Pitch Std DevHow much pitch varies overall41.9[null, null]18.324.1[null, null]25.2[null, null]35.6[null, null]19.918[null, null][null, null]26.559.980.433.3[null, null][null, null]66.332.452.954.228.3[null, null][null, null]26.1[null, null][null, null]2733.1[null, null][null, null][null, null]68.5[null, null]33.22436.372.1451856.157.535.883.434.4[null, null]77.430.2[null, null][null, null]38.637.744.9[null, null][null, null][null, null][null, null][null, null]59.558.347.438.626.863.9
V4Pitch Range (5th–95th)Usable pitch range excluding outliers130.928.656.274.328.478.349.1112.140.859.754.843.833.685194.2245.6101.829.831.4211.999.6172.2211.866.76.831.871.123.4103.680.6109.4126.740.330172.662.287.286.9114.8239.6147.657.8177.4168.7115.5235.2106.28.324295.56142.1124.3120.2132.430.528.137.864.542.8194.1193.2139.5125.986.5204.5
V5Pitch Contour PatternsWHERE pitch changes relative to content
V6Volume MeanAverage loudness (RMS energy)-11.6-14.9-13.5-11.2-16.3-10-16.4-16.1-14.2-18.8-13.2-11.4-17.3-15.9-10.8-14.8-15.7-31.3-15.2-21.1-15.2-13.1-12.6-9.9-10-11.5-19.8-11.6-11.2-14.3-12.2-20.4-23.9-10.8-11.4-21.3-17.2-11.4-14.6-22.1-12.2-13.7-13.6-22.4-17.6-19.6-14.8-5.5-9.7-12.5-12.5-18-13.9-16.8-14.4-17.5-18.9-20.1-19.3-15.4-13.7-12.4-12.7-14.7-16.8-12.3
V7Volume Std DevHow much volume varies9.243.58.77.122.97.722.28.435.210.3623.2296.910.68.913.350.14717.211.19.66.9418.220.41617.42010.85.776.360.838.86.534.111.510.67.820.15.5612.410.110.312.810.612.55.26.421.331.59.28.813.336.857.933.421.627.46.610.36.716.210.78.4
V8Dynamic RangeDifference between quiet and loud (P95–P5)30.915.529.120.3719.87.228.510.43319.38.810.223.433.631.146.316.51556.236.927.12113.16.45.950.45.66.938.61919.218.314.520.512.132.634.121.675.718.718.541.231.336.542.731.66.216.8196.59.131.330.545.511.919.49.513.78.921.932.921.547.830.425.3
V9Volume Envelope PatternsWHERE volume changes relative to content
V10Speech Rate (Mean)Average speaking speed116.1
V11Speech Rate (Std Dev)How much speaking speed varies3482.471.5
V12Speech Rate RangeSpread between slowest and fastest speech71204.5166.3
V13Speech Rate × ContentWHERE they speed up/slow down and why
V14Pause FrequencyHow often they pause (≥300ms)4.2100.423.200.53.64.35.14.21.10011.75.500090.16.9004.803.34.52.6001.72.78.1000.24.80.50.7
V15Pause Mean DurationAverage pause length0.50.400.40.40.500.40.50.50.40.50.5000.60.40000.50.30.600.70.50.60.80.4000.50.50.6000.50.40.40.6
V16Pause Max DurationLongest single pause1.10.500.40.71.300.60.60.90.80.80.7002.90.40002.40.30.900.71.003.01.40.5000.60.72.3000.80.60.40.6
V17Pause Duration VarianceHow much pause length varies (low=metronomic, high=strategic)
V18Pauses > 2.7s (Rowe)Educational threshold pauses (Rowe's research: 2.7s = thinking time)
V19Pause Placement ClassificationFUNCTION of each major pause: emphasis, dramatic, transitional, comedic
V20Filler Word Frequency"um", "uh", "like", "you know" as fraction of total words
V21Voice TextureQuality of voice: breathy, clear, gravelly, nasal, warm, resonant
V22Vocal Fry PresenceCreaky voice at ends of phrases
V23Uptalk PresenceRising intonation on statements (not questions)
V24Breathing PatternsHow breaths relate to speech rhythm82 breath pauses. Avg interval: 21.29s. Irregular — breath f2 breath pauses detected. Mean interval: 30.88s, Std: 0.0s, 0 breath pauses detected. Could not determine — no breathing9 breath pauses. Avg interval: 159.57s. Irregular — breath f34 breath pauses. Avg interval: 28.84s. Irregular — breath f142 breath pauses. Avg interval: 25.08s. Irregular — breath 0 breath pauses. Could not determine6 breath pauses detected. Mean interval: 22.71s, Std: 21.89s11 breath pauses detected. Mean interval: 22.54s, Std: 17.3399 breaths. Irregular — breath follows content, not clockModerate — natural variation. CV=0.76, 6 breath pauses detecCould not determine — only 2 breath pauses detected, mean in0 breaths. Interval: 0s. Could not determine0 breath pauses detected. Could not determine breathing patt139 breaths. Irregular — breath follows content, not clock6 breaths. Regular — consistent rhythm0 breaths. Interval: 0s. Could not determine0 breath pauses. Could not determine0 breath pauses. Could not determine108 breaths. Irregular — breath follows content, not clock1 breath pause detected. Could not determine breathing patte1 breaths. Could not determine0 breaths. Interval: 0s. Could not determine0 breath pauses detected. Could not determine breathing patt48 breath pauses. Avg interval: 26.98s. Irregular — breath f1 breath pauses. Could not determine3 breaths. Interval: 28.92s. Very regular — metronomic breat0 breaths. Could not determine0 breaths. Could not determineCould not determine — only 1 breath pause detected. Insuffic0 breaths. Interval: 0s. Could not determine0 breaths. Could not determine3 breaths. Very regular — metronomic breathing39 breath pauses detected. Mean interval: 12.70s, Std: 11.828 breaths. Irregular — breath follows content, not clock0 breath pauses. Could not determine
V25Emphasis TechniquePrimary method of stressing important words
V26Vocal Energy TrajectoryHow vocal energy changes over the full content (0–100%)even opening-to-middle; sustained to close; peak at ~5%. DecEven opening-to-middle; sustained to close; peak at ~5%. RemEven opening-to-middle; sustained to close; peak at ~85%. Laeven opening-to-middle; sustained to close; peak at ~5%. Deceven opening-to-middle; sustained to close; peak at ~5%. Deceven opening-to-middle; sustained to close; peak at ~5%. Deceven opening-to-middle; sustained to close; peak at ~85%. DeEven opening-to-middle; sustained to close; peak at ~65%. BuEven opening-to-middle; sustained to close; peak at ~15%. Freven opening-to-middle; sustained to close; peak at ~25%Builds from opening; sustained to close; peak at ~55%. DecilBuilds from opening; energy fades at end; peak at ~35%. Stroeven opening-to-middle; sustained to close; peak at ~45%. DeEven opening-to-middle; sustained to close; peak at ~5%. Verstrong opening, drops mid; sustained to close; peak at ~5%even opening-to-middle; sustained to close; peak at ~5%strong opening, drops mid; strong finish; peak at ~15%. Decistrong opening, drops mid; sustained to close; peak at ~25%.strong opening, drops mid; sustained to close; peak at ~5%. even opening-to-middle; sustained to close; peak at ~65%Even opening-to-middle; sustained to close; peak at ~5%. Frostrong opening, drops mid; sustained to close; peak at ~65%even opening-to-middle; sustained to close; peak at ~5%. DecEven opening-to-middle; sustained to close; peak at ~5%. Remeven opening-to-middle; sustained to close; peak at ~5%. Decstrong opening, drops mid; sustained to close; peak at ~5%. even opening-to-middle; sustained to close; peak at ~75%. Deeven opening-to-middle; sustained to close; peak at ~25%even opening-to-middle; energy fades at end; peak at ~25%Energy fades throughout — peak at opening (~5%), steady decleven opening-to-middle; sustained to close; peak at ~25%. Deeven opening-to-middle; sustained to close; peak at ~65%even opening-to-middle; sustained to close; peak at ~75%Even opening-to-middle; sustained to close; peak at ~45%. Eneven opening-to-middle; sustained to close; peak at ~95%even opening-to-middle; sustained to close; peak at ~85%. De
V27Prosodic RhythmOverall rhythmic quality: metronomic, conversational, dramatic, jazz-like
D3: Fluff
F1Teaching Content Ratio% of time on core topic vs everything else
F2Tangent FrequencyHow often they leave the main topic20001very low — ~1 brief tangent (addressing social media misconclow — ~1–2 brief digressions (caveat on extrapolating from slow — ~1–2 personal digressions; they serve to establish trumedium — ~3–4 brief opinion asides ('gurus are lying to you'11100411very low — ~1 brief digression (addressing hype around AI ag20000014600200100054003306000021221001005010
F3Tangent Avg DurationHow long each tangent lasts1200005~30s — single tangent addressing the 'everything is called a~45s — caveats are substantive but controlled; returns to ma~30–45s — personal moments are brief; credentials intro is ~~20–30s — opinion asides are punchy; doesn't dwell; returns 59050045820~20s — brief anti-hype aside quickly resolved back to the fr30000008875003001000060600040400550000451838206000200350200
F4Tangent Return Rate% of tangents that connect back to the main point100100100100100100% — always returns to main thread; tangent serves to rein100% — uses explicit verbal markers ('then back to that engi100% — all personal content is bookended (opening → path, cl95% — almost always returns; occasional opinion bleeds into 100100100100100100100100100% — returns via explicit verbal connector; never loses tr1001001001001001001008085100100100100100100100100100807510010010083n/an/an/a1000100100100n/a1001n/an/a80100n/a
F5Humor FrequencyHow often something is intended to be funny30000very low — essentially zero; professional/neutral tone throuvery low — virtually no humor; dry analytical register throulow — occasional light humor ('I know, I know') but not comemedium — light humor through confident irreverence ('they ha21202201near-zero — no humor detected; professional academic registe203510181501000113001501342143002003030011105211
F6Humor Type DistributionKinds of humor: wordplay, callback, self-deprecating, absurdist, etc.
F7Personal Story FrequencyHow often they share personal experience
F8Personal Story DurationHow long personal stories take on average300012790~15s when present — incidental personal context, not narrati0s — no personal stories present~75s total — credentials intro ~30s, closing backstory ~45s;short bursts — 10–20s personal credential/experience inserts86035025300~25s — credentials intro is compact; not narrative; no perso000000056000853000400090400015087500065060001860030200300300
F9Filler Content BreakdownWhat non-teaching time is made of: tangent, humor, meta, admin{"humor": 2, "personal_stories": 2, "tangents_unconnected": {"humor": 0, "personal_stories": 0, "promo": 100}{"humor": 0, "personal_stories": 0, "promo": 100}{"humor": 0, "personal_stories": 100, "promo": 0}{"humor": 0, "personal_stories": 85, "promo": 15}sponsor segment (~38s); transition signposting (~20s); closiclosing audience question (~15s); total non-teaching ~15s ofcredentials intro (~30s); course recommendations as personalcredentials opening (~30s); personal experience inserts acro{"humor": 30, "personal_stories": 40, "promo": 30}{"humor": 1, "personal_stories": 2, "tangents_unconnected": {"humor": 60, "personal_stories": 30, "promo": 10}{"humor": 0, "personal_stories": 20, "promo": 80}{"humor": 80, "personal_stories": 0, "promo": 20}{"humor": 5, "personal_stories": 5, "tangents_unconnected": {"humor": 0, "personal_stories": 50, "promo": 50}{"humor": 10, "personal_stories": 0, "promo": 0, "embedded_ccredentials intro (~25s); like/subscribe outro (~20s); total{"sponsor_cta": 10, "transitions_filler_words": 5, "intro_ou{"preamble": 5, "recap": 0, "meta_commentary": 0, "humor": 0{"narrative_setup": 80, "meta_frame": 15, "hook": 5}{"analogy_vehicle": 85, "statistical_aside": 10, "hook": 5}{"argument": 90, "example": 5, "challenge": 5}{"teaching": 100}{"humor": 5, "personal_stories": 0, "tangents_unconnected": {"humor": 60, "character_performance": 25, "actual_teaching"{"humor": 15, "personal_stories": 7, "tangents_unconnected":{"cta": 15, "teaching": 85}{"cta": 15, "news_facts": 60, "interpretation": 25}{"personal_story": 85, "greeting_asides": 15}{"teaching_prescription": 90, "personal_disclosure": 5, "emp{"promo": 1, "total_seconds": 4}{"transitions_filler_words": 3, "intro_outro": 2, "humor": 0{"podcast_promo_intro": 7, "inspirational_rhetoric": 20}{}{}{"personal_stories": 20, "sponsor_cta": 10, "transitions_fil{"humor": 12, "personal_stories": 25, "tangents_unconnected"{}{"intro_self_id": 3, "follow_cta": 2, "humor_beat": 2, "tota{"humor": 8, "personal_stories": 4, "tangents_unconnected": {"humor": 5, "tangents_unconnected": 4, "intro_outro": 3, "t{"disclaimer_intro": 10, "personal_anecdote_evidence": 8, "t{"humor": 28, "personal_stories": 22, "tangents_unconnected"{"failed_take": 9, "repeated_okays": 9, "celebration": 4, "c{"attribution_setup": 5, "total_non_pure_argument": 5}{"filler_words": "kinda (1 instance)", "transitions": "minim{"personal_narrative": 0.7, "clip_context": 0.2, "teaching":{"enthusiasm_reactions": "it's exciting, that's nice", "fill{"humor": 0.02, "personal_stories": 0.12, "tangents_unconnec{"self_promotion": 0.65, "humor_filler": 0.2, "meta_commenta{"humor": 0.0, "personal_stories": 0.0, "tangents_unconnecte{"meta_commentary": 40, "personal_stories": 30, "humor": 10,{"humor": 0.0, "personal_stories": 0.06, "tangents_unconnect{"series_branding": "Part 912 tag ~2s"}{"humor": 0.01, "personal_stories": 0.03, "tangents_unconnec{"enthusiasm_reactions": "'so cool', 'my favorite thing ever{"dialogue_reactions": "'OK', 'Cool', 'No', 'That's crazy', {"relatable_opener": "'I mean, it should, but it doesn't' ~2{"humor": 18, "personal_stories": 20, "tangents_unconnected"{"humor": 3, "personal_stories": 0, "tangents_unconnected": {"humor": 0.02, "personal_stories": 0.03, "tangents_unconnec{"authority_building": "first 20s credentials establishment"
F10Strategic vs Accidental FluffWhether non-core content reinforces lesson, builds trust, or serves no purpose55544strategic — sponsor placement at ~3:30 mark (early but post-strategic — the caveat structure is deliberate analytical frstrategic — personal content placed at start (trust) and endstrategic — personal credential inserts reinforce authority 42554555strategic — anti-hype positioning is deliberate brand differ4555555525515555455345fully_strategic45appears_spontaneous_likely_strategic5intentionally_posted_BTSfully_strategicstrategicstrategicmixed3mixed455strategic5mostly_strategicstrategicstrategic455strategic
F11Attention Reset PatternsRegular breaks in dense content that re-engage the audience
D4: 4th Wall
W1Direct Address FrequencyHow often they say "you" to the audience
W2Direct Address %What fraction of sentences address the audience41.517.93610017.642.929.220.47025200037.101213.51.925.31.5710010052.415.2
W3Audience Reference StyleHow they frame the audience: "you", "we", "people", "one"
W4Eye Contact BehaviorHow they relate to camera: direct, looking away, alternating
W5Meta-Commentary FrequencySelf-aware asides about the content/process itself500105234161119124618100011300010410007401862105000022583050007450
W6Audience Assumption LevelHow much prior knowledge they assume: nothing to expertintermediatesome_exposuresome_exposureadvancedcomplete_beginnerintermediatesome_exposurecomplete_beginnersome_exposure
W7Invitation to ParticipateExplicit invitations for audience to think/do something425171353
W8Permission SignalsMoments giving permission to not know/be confused10000015832000310031500100211113121000420245150101102322031114110
W9Audience Surrogate UseUsing a co-host/character as stand-in for the audience31000001025001410000700000100nonenoneshared_struggle01000030101120601nonenonenone0none131none1skeptical_parent_or_educatordialogue_partnerconfused_developer422named_skeptic_marcella
W10Response SolicitationAsking the audience to respond: "right?", "make sense?"0rhetorical_questionscomment_ctanonenonerhetorical_questionsrhetorical_questionsrhetorical_questionsrhetorical_questionsnone0nonegenuine_questionsnonegenuine_questionsgenuine_questionsnonerhetorical_questionsrhetorical_questionsnonegenuine_questionsnonenoneimplicit_reflectionnonenonenonegenuine_questionsfollow_save000commentsrhetorical_questionsnonenonenoneimplicit_shadownonerhetorical_questionsnone1rhetorical_questionsgenuine_questions0000000genuine_questions0nonerhetorical_questionsgenuine_questions0none0000rhetorical_questionsgenuine_questions1
W11Vulnerability DisplayShowing uncertainty, mistakes, learning moments21121121232213331111411111331052122411321022244001013332011003220
D5: Visuals
Composition
VA1Framing Type DistributionShot sizes used: close-up, medium, wide, etc.
VA2Framing Changes / MinHow often the framing changes
VA3Subject PositionWhere the subject sits in frame: left, center, right thirdcenterleft_thirdcentercenterleft_thirdleft_thirdcenterright_thirdcenterright_thirdcentercentercenterright_thirdcentercenterleft_thirdright_thirdleft_thirdcentercentercentercenterleft_thirdcenterright_thirdcentercenterleft_thirdcentercenterright_thirdcentercentercenterright_thirdright_thirdcentercenterright_thirdright_thirdright_thirdcentercentercenterright_thirdcentercentercentercentercentercentercenterright_thirdleft_thirdleft_thirdcentercentercentercenterleft_thirdright_thirdcenter
VA4HeadroomSpace above the subject's head as % of frame5153.232.232.262.763.213.538.652.765.720.931.622.981.114.814.818.86732.624.624.624.613.211.238.13243.546.130.718.216.68.52645.263.916.657.230.628.454.725.625.616.65231.727.831.637.547.814.531.631.631.637.857.324.12849.226.911.643.359.537.9
VA5Look SpaceSpace in the direction the subject faces
VA6Camera AngleAngle relative to subject: eye-level, low, high, dramatic
VA7Camera MovementWhether camera moves: static, drift, handheld, dolly, tracking, zoom
Color & Light
VB1Dominant Color PaletteMain colors in the frame (K-means clustering → 5 hex values)[{"hex": "#0e0b0d", "rgb": [14, 11, 13], "percentage": 36.8}[{"hex": "#f8f7f9", "rgb": [248, 247, 249], "percentage": 46[{"hex": "#28201f", "rgb": [40, 32, 31], "percentage": 38.4}[{"hex": "#585b52", "rgb": [88, 91, 82], "percentage": 30.4}[{"hex": "#a49fa3", "rgb": [164, 159, 163], "percentage": 31[{"hex": "#1b140f", "rgb": [27, 20, 15], "percentage": 33.5}[{"hex": "#111213", "rgb": [17, 18, 19], "percentage": 79.9}[{"hex": "#121213", "rgb": [18, 18, 19], "percentage": 73.8}[{"hex": "#000000", "rgb": [0, 0, 0], "percentage": 44.1}, {[{"hex": "#342234", "rgb": [52, 34, 52], "percentage": 45.5}[{"hex": "#2d181a", "rgb": [45, 24, 26], "percentage": 28.6}[{"hex": "#64746c", "rgb": [100, 116, 108], "percentage": 33[{"hex": "#252a45", "rgb": [37, 42, 69], "percentage": 33.9}[{"hex": "#f7f6f1", "rgb": [247, 246, 241], "percentage": 33[{"hex": "#000000", "rgb": [0, 0, 0], "percentage": 58.3}, {[{"hex": "#000000", "rgb": [0, 0, 0], "percentage": 95.7}, {[{"hex": "#02131e", "rgb": [2, 19, 30], "percentage": 67.0},[{"hex": "#040305", "rgb": [4, 3, 5], "percentage": 50.0}, {[{"hex": "#010101", "rgb": [1, 1, 1], "percentage": 73.4}, {[{"hex": "#1b1111", "rgb": [27, 17, 17], "percentage": 28.1}[{"hex": "#1b1111", "rgb": [27, 17, 17], "percentage": 28.1}[{"hex": "#000001", "rgb": [0, 0, 1], "percentage": 62.4}, {[{"hex": "#0a090c", "rgb": [10, 9, 12], "percentage": 78.3},[{"hex": "#201716", "rgb": [32, 23, 22], "percentage": 25.9}[{"hex": "#000000", "rgb": [0, 0, 0], "percentage": 98.3}, {[{"hex": "#000000", "rgb": [0, 0, 0], "percentage": 98.3}, {[{"hex": "#f9f8f4", "rgb": [249, 248, 244], "percentage": 80[{"hex": "#261e24", "rgb": [38, 30, 36], "percentage": 48.5}[{"hex": "#1d1e1c", "rgb": [29, 30, 28], "percentage": 62.7}[{"hex": "#57452d", "rgb": [87, 69, 45], "percentage": 32.9}[{"hex": "#f8f6f4", "rgb": [248, 246, 244], "percentage": 75
VB2Color TemperatureWarm vs cool overall feel1200055005500550055005500550077775500550055005500336055005500550081425500550055005500550055007772334655005500793255005500550055005500120005500550055005500550055005500348755003317331755007772550012000340055005500550055005500550055005500550033865500550055005500550077505500
VB3Contrast RatioDifference between lightest and darkest areas0.30.60.80.80.40.60.40.30.81.00.60.60.80.40.80.80.60.60.90.50.50.50.60.30.60.70.70.60.90.60.70.80.10.40.40.80.80.50.70.80.80.70.40.70.70.60.70.60.90.50.80.60.30.00.00.00.50.90.30.70.90.60.70.60.60.40.9
VB4Saturation LevelHow vivid the colors are
VB5Lighting DirectionWhere the main light comes from: front, side, back, diffuse
VB6Background StyleWhat's behind the subject: solid, home, studio, outdoor, blurred
VB7Color ConsistencyHow much the palette changes across content0.70.80.90.90.60.80.80.80.90.90.80.80.80.90.90.90.80.70.20.80.80.80.90.30.90.90.70.70.80.91.00.80.70.60.30.90.71.00.90.70.90.90.80.80.80.90.90.90.60.80.70.90.70.50.50.50.70.80.71.00.80.80.70.90.60.70.9
Text & Graphics
VC1Text Overlay FrequencyHow often text appears on screen
VC2Text StyleVisual characteristics: font, size, color, position, animation
VC3Text–Speech RelationshipHow text relates to speech: reinforcing, supplementary, redundant
VC4Graphic FrequencyNon-text visual elements: icons, diagrams, illustrations
VC5Lower Third UseName/title cards: present, absent, style
VC6Emoji/Sticker UseInformal visual elements overlaid on video
VC7Caption/Subtitle StyleIf subtitles present: burned-in, word-by-word, animated, etc.
Motion & Editing
VD1Cut FrequencyHow often the video cuts to a different shot0.635008.71.45.80.31.6008.160001.21000004.89.101.228.618.500200.722.20001.23.918.42.736.66.62.5150.909.32.7000000.616202.16.39.57.20.40.54.7
VD2Transition TypesWhat happens between cuts: hard cut, crossfade, wipe, zoom, jump cut
VD3Subject Movement LevelHow much the person moves within a shot (0–10 scale)
VD4Gesture Type DistributionWhat hands/body communicate: illustrative, emphatic, rhythmic, pointing
VD5Gesture FrequencyHow often they gesture
VD6B-Roll FrequencyHow often non-subject footage is used
VD7B-Roll TypeKind of supplementary footage: stock, original, diagram, screen recording
VD8Zoom/Pan EventsIn-post camera movement effects (Ken Burns etc.)
VD9Visual Pacing RhythmTemporal feel of edits: metronomic, accelerating, varied, chaotic
VD10Facial Expression RangeRange of emotions: deadpan(1) to theatrical(5)
D6: Polish
Production Quality
P1Audio Quality (SNR)Signal-to-noise ratio of the audio26.728.431.233.827.824.222.625.631.32644.644.932.334.626.621.338.932.720.835.423.528.432.550.721.728.33835.628.933.53226.546.522.723.636.522.131.22744.638.631.7
P2Video ResolutionTechnical quality from file metadata1280x720720x12801080x19201080x19201280x7201280x7201280x7201280x720540x9601280x7201080x19201080x1920720x12801280x7201080x19201080x19201280x7201280x720720x720480x854480x854480x8541080x19201280x720720x1280720x12781280x720720x12801080x19201080x19201080x19201080x1920720x12801280x7201080x19201080x1920720x12801080x19201280x7201280x7201080x1920720x12801280x7201280x7201280x7201280x7201080x19201080x19201080x19201080x1920720x12801080x19201280x720320x240320x240320x2401280x7201080x19201280x720360x6401080x19201080x19201080x19201280x7201280x7201280x7201080x1920
P3Edit CleanlinessHow smooth the editing is (1–4 scale)
P4Color GradingWhether color has been professionally treated
P5Consistency Across ContentHow much production quality varies piece to piece
P6Polish–Content ContrastGap between production sophistication and content informality
P7Production Elements CountHow many features: intro, outro, music, SFX, graphics, lower thirds
P8Music / Sound DesignPresence and role of non-speech audio
P9Thumbnail QualityHow much effort the thumbnail shows (1–4 scale)
P10Polish Strategic PurposeWHY this level of polish was chosen: trust, authenticity, brand, budget
Human Error
P11Mistake FrequencyHow often errors occur per piece10000122105011300120400000030030050001330023123000110631021104111
P12Mistake Type DistributionKinds of mistakes: verbal slip, factual error, confusion, self-correction{"verbal_slip": 1, "self_correction": 0, "confusion_moment":{"verbal_slip": 2, "self_correction": 2, "confusion_moment":{"verbal_slip": 1, "self_correction": 1, "confusion_moment":{"verbal_slip": 0, "self_correction": 0, "confusion_moment":{"verbal_slip": 1, "self_correction": 1, "confusion_moment":{"verbal_slip": 2, "self_correction": 2, "confusion_moment":{"verbal_slip": 2, "self_correction": 1, "confusion_moment":{"verbal_slip": 2, "self_correction": 1, "confusion_moment":{"verbal_slip": 1, "self_correction": 0, "confusion_moment":
P13Mistake SeverityHow serious: trivial, noticeable, understanding-threatening{"trivial": 1, "noticeable": 0, "understanding_threatening":{"trivial": 0, "noticeable": 0, "understanding_threatening":{"trivial": 0, "noticeable": 0, "understanding_threatening":{"trivial": 0, "noticeable": 0, "understanding_threatening":{"trivial": 0, "noticeable": 0, "understanding_threatening":{"trivial": 1, "noticeable": 0, "understanding_threatening":{"trivial": 2, "noticeable": 0, "understanding_threatening":{"trivial": 2, "noticeable": 0, "understanding_threatening":{"trivial": 1, "noticeable": 0, "understanding_threatening":{"trivial": 0, "noticeable": 0, "understanding_threatening":{"trivial": 4, "noticeable": 1, "understanding_threatening":{"trivial": 0, "noticeable": 0, "understanding_threatening":{"trivial": 1, "noticeable": 0, "understanding_threatening":{"trivial": 1, "noticeable": 0, "understanding_threatening":{"trivial": 2, "noticeable": 1, "understanding_threatening":{"trivial": 0, "noticeable": 0, "understanding_threatening":{"trivial": 0, "noticeable": 0, "understanding_threatening":{"trivial": 1, "noticeable": 0, "understanding_threatening":{"trivial": 2, "noticeable": 1, "understanding_threatening":none{"trivial": 3, "noticeable": 1, "understanding_threatening":nonenonenonenone{"trivial": 0, "noticeable": 0, "understanding_threatening":none{"trivial": 3, "noticeable": 0, "understanding_threatening":nonenonelownonenone{"trivial": 4, "noticeable": 1, "understanding_threatening":nonenonenoneminor{"trivial": 3, "noticeable": 0, "understanding_threatening":{"trivial": 2, "noticeable": 1, "understanding_threatening":none{"trivial": 2, "noticeable": 0, "understanding_threatening":{"trivial": 2, "noticeable": 1, "understanding_threatening":2{"trivial": 1, "noticeable": 1, "understanding_threatening":2nonenoneminimal{"trivial": 1, "noticeable": 0, "understanding_threatening":none{"trivial": 5, "noticeable": 1, "understanding_threatening":{"trivial": 3, "noticeable": 0, "understanding_threatening":{"trivial": 1, "noticeable": 0, "understanding_threatening":none{"trivial": 2, "noticeable": 0, "understanding_threatening":minimalminornone{"trivial": 3, "noticeable": 1, "understanding_threatening":{"trivial": 1, "noticeable": 0, "understanding_threatening":{"trivial": 1, "noticeable": 0, "understanding_threatening":minimal
P14Mistake TimingWHEN in the content mistakes happen: early, middle, late, at key moment{"early": 0, "middle": 1, "late": 0}{"early": 0, "middle": 0, "late": 0}{"early": 0, "middle": 0, "late": 0}{"early": 0, "middle": 0, "late": 0}{"early": 0, "middle": 0, "late": 0}{"early": 0, "middle": 0, "late": 0}{"early": 1, "middle": 3, "late": 1}{"early": 0, "middle": 0, "late": 0}{"early": 0, "middle": 1, "late": 0}{"early": 0, "middle": 1, "late": 0}{"early": 0, "middle": 1, "late": 2}{"early": 0, "middle": 0, "late": 0}{"early": 0, "middle": 0, "late": 0}none{"early": 1, "middle": 3, "late": 0}nonenonenonenone{"early": 0, "middle": 0, "late": 0}nonenonenonethroughoutnonenone{"early": 1, "middle": 3, "late": 1}nonenonenonemid{"early": 1, "middle": 2, "late": 0}none{"early": 0, "middle": 2, "late": 1}37{"early": 0, "middle": 2, "late": 0}0n/an/amid_demo{"early": 0, "middle": 1, "late": 0}n/a{"early": 1, "middle": 3, "late": 2}{"early": 0, "middle": 2, "late": 1}{"early": 0, "middle": 1, "late": 0}n/a{"early": 1, "middle": 1, "late": 0}openingearlyn/a{"early": 2, "middle": 1, "late": 1}{"early": 0, "middle": 1, "late": 0}{"early": 0, "middle": 1, "late": 0}mid_content
P15Mistake-to-Correction RatioHow many mistakes get corrected040671001003333250
P16Correction StyleHow they handle mistakes: smooth, explicit, comedic, ignoredignorednonenonenonenonesmooth_recoverysmooth_recoverysmooth_recoverysmooth_recoverynonesmooth_recoverynoneignoredignoredexplicit_acknowledgmentnonenonesmooth_recoverysmooth_recoverynonecomedic_spinnonenonenonenonesmooth_recoverynonesmooth_recoverynonenonenonenonenoneexplicit_acknowledgmentnonenonenonenonesmooth_recoverysmooth_recoverynonesmooth_recoveryexplicit_acknowledgmentimmediate_restartcomedic_spinexplicit_collaborativen/an/an/aignoredn/aexplicit_acknowledgmentsmooth_recoverysmooth_recoveryn/aignoredn/an/an/aexplicit_acknowledgmentcomedic_spinignoredn/a
P17Humanization EffectWhether mistakes increase (+) or decrease (-) trust
P18Mistake–Content Placement% of mistakes at crucial explanatory moments vs non-critical
D7: Rhythm
R1Energy ArcComposite trajectory of intensity over full content (0–100%)even opening-to-middle; sustained to close; peak at ~5%Even opening-to-middle; sustained to close; peak at ~5%. RemEven opening-to-middle; sustained to close; peak at ~85%. Laeven opening-to-middle; sustained to close; peak at ~5%even opening-to-middle; sustained to close; peak at ~5%even opening-to-middle; sustained to close; peak at ~5%even opening-to-middle; sustained to close; peak at ~85%even opening-to-middle; sustained to close; peak at ~5%Even opening-to-middle; sustained to close; peak at ~65%. EnEven opening-to-middle; sustained to close; peak at ~15%. Freven opening-to-middle; sustained to close; peak at ~25%Wave pattern — even opening-to-middle with dips at 20% and 7Builds from opening; sustained to close; peak at ~55%. EnergBuilds from opening; energy fades at end; peak at ~35%. Clas{"decile_energy_db": [-15.1, -18.4, -17.6, -14.7, -15.5, -11even opening-to-middle; sustained to close; peak at ~45%Even opening-to-middle; sustained to close; peak at ~5%. Verstrong opening, drops mid; sustained to close; peak at ~5%even opening-to-middle; sustained to close; peak at ~5%strong opening, drops mid; strong finish; peak at ~15%strong opening, drops mid; sustained to close; peak at ~25%strong opening, drops mid; sustained to close; peak at ~5%even opening-to-middle; sustained to close; peak at ~65%Even opening-to-middle; sustained to close; peak at ~5%. Frostrong opening, drops mid; sustained to close; peak at ~65%even opening-to-middle; sustained to close; peak at ~5%Even opening-to-middle; sustained to close; peak at ~5%. Exteven opening-to-middle; sustained to close; peak at ~65%builds from opening; energy fades at end; peak at ~45%even opening-to-middle; sustained to close; peak at ~5%strong opening, drops mid; sustained to close; peak at ~5%even opening-to-middle; sustained to close; peak at ~75%even opening-to-middle; sustained to close; peak at ~25%even opening-to-middle; energy fades at end; peak at ~25%Descending arc — energy peaks at opening (~5%) and steadily even opening-to-middle; sustained to close; peak at ~35%even opening-to-middle; sustained to close; peak at ~55%even opening-to-middle; sustained to close; peak at ~25%even opening-to-middle; sustained to close; peak at ~65%even opening-to-middle; sustained to close; peak at ~75%Even opening-to-middle; sustained to close; peak at ~45%. Eneven opening-to-middle; sustained to close; peak at ~95%even opening-to-middle; sustained to close; peak at ~85%
R2Tension-Release CyclesBuild-then-release patterns where multiple parameters rise then drop41001445845212423450621102161111152130451368262211130344172315543
R3Format ConsistencyHow similar the rhythm is across different pieces by same educator
R4Attention Reset FrequencyHow often the format shifts to re-engage9012075154590606045
R5Pacing VariationHow much temporal feel changes within a single piece2.80.10.53.90.8010.87.83400005.311.302003.682.471.53.321.43.53.72.1
R6Climax PlacementWhere the most intense moment falls as % through content55855558565152555553555455551525565565555575252553555256575459585
R7Segment Length ConsistencyHow regular the chunks are (coefficient of variation)mostly_regularvery_regularvery_regularmostly_regularmostly_regularmostly_regularmostly_regularvery_regularvery_regularvery_regularmostly_regularmostly_regularmostly_regularvariedvariedmostly_regularvariedmostly_regularmostly_regularsingle_blockvariedsingle_narrativecandidate_unitsargument_chainuniform_parallelvery_regularloose_vignetteirregularsingle_arcsingle_arcstream_of_consciousnessargument_then_stepshighvery_regularlowhighhighperfectmostly_regularmostly_regularhighhighmostly_regularvery_regularnonemostly_regularnonenoneconsistent_news_formatconsistent_narrative_voiceconsistent_demo_formatmostly_regularconsistent_conversationalmostly_regularmostly_regularmostly_regularsingle_segment_formatvery_regularconsistent_review_formatconsistent_qa_dialogueconsistent_explainer_formatmostly_regularmostly_regularmostly_regularconsistent_numbered_debunk