Understanding bias in proportion production.
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
The Stevens exponent (beta) can be obtained from proportion estimation judgments using the power model. In this article, the authors extend that model to proportion production, in which the relative magnitudes of 2 stimuli are adjusted to correspond to a numeric proportion (e.g., 1/4 or .25). The model predicts that when beta < 1, small proportions are underproduced, and large proportions are overproduced, but it predicts the reverse when beta > 1, which is the opposite of the predicted patterns for estimation. Eight participants estimated and produced magnitudes and proportions with spatial volume (beta < 1; Experiment 1) and color saturation (beta > 1; Experiment 2). The model's predictions were generally supported. An extension of the model using reference points can account for multicycle patterns shown by some participants.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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 it