Explanation and Misrepresentation in the Laboratory
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
We report the results of an experiment designed to examine the effect of opportunity to provide an explanation for inaccurate results and predictability of behavior on managersâ?? reporting bias and investorsâ?? ability to decipher the bias. We conduct 20 experimental sessions, each comprised of one manager and three or four investors. The manager has an incentive, in general, to inflate investorsâ?? expectations and investors have an incentive to accurately predict value. We find that the manager reports with an upward bias a majority of the time. The magnitude of the bias, however, is lessened considerably when the managerâ??s reporting behavior is unpredictable and the manager has an opportunity to explain inaccurate (biased) reports. The data suggest that under such conditions the manager seeks to avoid reporting inaccurately and having to choose an explanation. We also find that investors adapt to the managerâ??s behavior and, strikingly, anticipate that explanation dampens reporting bias.
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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 | 0.000 | 0.000 |
| 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