THE SHAPE OF THE UNDERLYING DISTRIBUTIONS IN ABSOLUTE IDENTIFICATION EXPERIMENTS
Bibliographic record
Abstract
In signal-detection analyses of one-dimensional, n-alternative, absolute-identification (AI) experiments it is usually assumed that the n stimuli give rise to n equal-variance, normal- distributions (EVNDs) along a uni-dimensional decision axis. However, Parker et al. (2002) have argued that equal-variance Laplace distributions (EVLDs) provide a better fit to AI data. This result is somewhat counter-intuitive, especially if the distribution of effects along the decision axis are thought to arise from noise (or an accumulation of small errors) in the decision process, which, according to the central limit theorem, should give rise to normal distributions. Here, we show that even when the data from AI experiments are generated from EVNDs, EVLDs will characterize the results, whenever the data are averaged across sessions (either within- or between-subjects) in which the underlying acuity (separation between distributions) is changing, a situation that is likely to occur whenever there are changes in gain-control .
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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 | 0.029 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".