Illusions of competence for phonetically, orthographically, and semantically similar word pairs.
Bibliographic record
Abstract
Illusions of competence are thought to arise when judgements of learning (JOLs) made in the presence of intact cue-target pairs during study create a "foresight bias," such that JOLs are inflated by the apparent association between a cue and a target, despite the lack of benefit this association has for recall performance. For example, Castel, McCabe, and Roediger (2007) recently demonstrated an illusion of competence for identical word pairs (mouse-mouse). In two experiments, the authors examined possible sources for this over confidence, including phonetic, semantic, and orthographic similarity. An illusion of competence was found for homophones, synonyms, orthographically similar, and unrelated items, whereas no illusion of competence was found for word pairs with a relatively high forward-semantic association. Self-paced study times indicated that encoding fluency was not closely associated with the magnitude of over confidence. Error data revealed participants may have been engaging in strategic responding in order to maximise correct recall. Our results underscore the importance of considering factors that influence both JOLs and recall performance when considering sources of (mis)calibration in absolute accuracy.
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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 | 0.018 |
| 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 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".