Kognitivnye korrelyaty raspoznavaniya obmana v pozhilom i starcheskom vozraste
Bibliographic record
Abstract
Aktual'nost' predlagaemogo issledovaniya obuslovlena neobhodimost'yu poiska putej snizheniya uyazvimosti lic pozhilogo i starcheskogo vozrasta k obmanu i moshennicheskim dejstviyam. Cel'yu issledovaniya bylo ocenit' kognitivnye korrelyaty raspoznavaniya obmana v pozhilom i starcheskom vozraste. Ob"em vyborki sostavil 87 ispytuemyh pozhilogo i starcheskogo vozrasta (60–89 let) — 38 muzhchin i 49 zhenshchin. Ispol'zovali metodiku MoCA (Montreal Cognitive Assessment); test Salli–Enn; Pragmatic intervention short stories Winner’s Task; eksperimental'nuyu metodiku Read the Mind in the eye (RMET); shkalu samoocenki Dembo–Rubinshtejn; shkalu samoocenki doveriya. Na osnovanii poluchennyh rezul'tatov issledovaniya vyyavleny kognitivnye korrelyaty raspoznavaniya obmana v pozhilom i starcheskom vozraste. Dostoverno ustanovleno, chto s vozrastom, po mere stareniya vne zavisimosti ot urovnya obrazovaniya proiskhodit snizhenie kognitivnogo urovnya, chto, v celom, yavlyaetsya zakonomernym v processe normativnogo stareniya. Eti izmeneniya privodyat k snizheniyu urovnya ponimaniya modeli psihicheskogo, chto, v svoyu ochered', zatrudnyaet raspoznavanie emocij i povyshaet uroven' doveriya. Empiricheskoe issledovanie podtverdilo gipotezu o nalichii korrelyacii mezhdu kognitivnym urovnem i sposobnost'yu raspoznavat' obman. CHem nizhe obshchij kognitivnyj uroven', tem huzhe raspoznaetsya obman i tem bolee doverchivym stanovitsya chelovek.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".