POSTER: Overclaiming as Convenient Proxy for Confidence and Overconfidence
Bibliographic record
Abstract
Introduction Ideally, measuring over confidence (specifically, overestimation) requires an objective measure of ability to contrast with self-estimates. Administering such parallel tests can costly. Furthermore, if overconfidence is measured as a difference, it remains confounded by the reference ability and self-estimates. Overclaiming is an efficient, unobtrusive technique that compares claiming familiarity of genuine items (Reals) against claiming familiarity with fake items (Foils). Objectives To explore how the overclaiming technique distinguishes between respondents’ actual knowledge and their perceived knowledge as predictors of academic performance. Methodology Undergraduate students were given both a vocabulary test and then an overclaiming measure of vocabulary, as well as other knowledge tests which included confidence ratings for general knowledge items. Their overall grades in an introductory psychology course were also collected (not self-report). Overconfidence was calculated as the difference between a respondent’s standardized confidence ratings and their standardized correct knowledge score. Results Vocabulary ability measured by Overclaiming (Reals-claiming minus Foils-claiming) strongly correlated with a conventional vocabulary ability measure ( r (163) = .77***, CI .95 = [.69, .82]), and moderately with course grade, r (163) = .37***, CI .95 = [.23, .50]. Overconfidence was necessarily confounded by confidence and general knowledge measures (e.g. r = .55), yet negatively predicted course grade, r (162) = -.30***, CI .95 = [-.43, -.15]. Reals-claiming captured confidence ( r (162) = .33***, CI .95 = [.19, .46]) and Foils-claiming captured overconfidence, r (162) = .24**, CI .95 = [.08, .37]. Regression models showed no overlap between the two associations. Conclusions Assessing overconfidence via combined ability and reported confidence is onerous and yields confounded measures which can’t be combined in predictive regression models. Our vocabulary overclaiming technique, despite covering a different knowledge area, captures general confidence and overconfidence in a way that can be combined for predicting academic outcomes. NOTE: * p < .05, ** p < .01, *** p < .001
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.113 | 0.020 |
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".