Recognizing National Hockey League greatness with an ignorance-based heuristic.
Bibliographic record
Abstract
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.
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".