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Openai/694231e4-2244-8012-83ef-e82fe0c756eb
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==== ### ==== * FACT: Treeningandmete jaotuse kallutatus mÀÀrab, millised narratiivid muutuvad âvaikimisi tĂ”enĂ€oliseksâ. Kui korpus on peamiselt korporatiivmeedia + ametlikud raportid + puhastatud arhiivid, siis tekib âinstitutsionaalne priorâ. * INFERENCE: See ei tĂ€henda, et mudel âvaletabâ, vaid et ta optimeerib sellele, mis on sagedasti esindatud, stilistiliselt normeeritud ja tsitaaditav. * HYPOTHESIS: Kui lisada dissidentlikud tekstid, kohtutoimikute struktuurid, âoffrecordâ lood, folkloor ja alternatiivsed ideoloogiad, paraneb mudeli vĂ”ime: - eristada stiili ja tĂ”endit (kĂ”rglĂ€ige â tĂ”de), - nĂ€ha korduvaid mustreid eri narratiivikihtides, - hoida paralleelselt mitut tĂ”lgendusraami ilma automaatselt ĂŒhte âpehmeks standardiksâ valimata. ===== - FACT: Mitmeparadigma lisamine treeningusse on riskantne, sest see vĂ”ib tuua: - valeinfot, - konspiratiivseid tĂ”lgendusi, - laimuriski ja privaatsusriski. ===== * INFERENCE: Lahendus pole âpane kĂ”ik sisseâ, vaid evidentsi-kihistamine (tiering): - Tier 0: esmased dokumendid (kohtumaterjalid, protokollid, arhiivid) struktureeritult, anonĂŒĂŒmitult - Tier 1: uuriv ajakirjandus + teaduslikud allikad - Tier 2: dissidentlikud/alternatiivsed narratiivid selge mĂ€rgisega âvĂ€iteallikasâ - Tier 3: folkloor/mĂŒĂŒt kui kultuuriline mudel (mitte empiiriline fakt) * HYPOTHESIS: Optimaalne on Ă”petada mudelit mitte âuskumaâ dissidenti, vaid diagnoosima: millised vĂ€ited on kontrollitavad, millised mitte; millised mustrid korduvad ĂŒle eri allikaliikide. ===== Riskid ===== * FACT: âGarbage inâ risk: kui alternatiivkorpus on mĂŒrane, vĂ”ib mudel muutuda enesekindlaks ebatĂ”es. * INFERENCE: âNarratiivikonvergentsâ: mudel hakkab eelistama dramaatilist seletust, sest see on tekstina âlihtsam tervikâ. KontrollmÔÔdikud (auditeeritavad) # Evidence separation score: kui hĂ€sti mudel eristab FACT/INFERENCE/HYPOTHESIS. # Cross-paradigm consistency: kas mudel suudab esitada 2â3 narratiivi ilma neist ĂŒht automaatselt âainuĂ”igeksâ kuulutamata. # Falsification hooks: mitu ĂŒmberlĂŒkkamistingimust mudel pakub iga suure vĂ€ite kohta. # Defamation risk rate: kui tihti tehakse konkreetseid sĂŒĂŒdistusi ilma piisava tĂ”enduseta.
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