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Record W2037660995 · doi:10.1037/cjep2006005

Recognizing National Hockey League greatness with an ignorance-based heuristic.

2006· article· en· W2037660995 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHeuristicLeaguePsychologyIgnoranceGreatnessComputer scienceArtificial intelligenceSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

This study examined whether people adhered to the recognition heuristic (i.e., inferred that a recognized hockey player had more total career points than an unrecognized player) and whether using this heuristic could yield accurate decisions. On paired comparisons, having participants report whether they recognized each player plus any knowledge they had about each player permitted players to be classified as either unrecognized (UR), merely recognized (MR), or recognized with additional knowledge (RK), thus producing six possible trial types. Participants adhered to the recognition heuristic on 95% of MR-UR trials and were accurate on 81% of those trials. They chose the recognized player on 98% of RK-UR trials, yielding 94% accuracy. Women had less knowledge and recognized fewer players than men, yet they were nearly as accurate as men. Future research should examine the conditions under which the recognition heuristic is an adaptive strategy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.061
GPT teacher head0.273
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it