THE SHAPE OF THE UNDERLYING DISTRIBUTIONS IN ABSOLUTE IDENTIFICATION EXPERIMENTS
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.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it