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
Predictions about the future are often inaccurate, but the direction of prediction errors may vary. Contrary to research on the intention-behavior gap, where people fail to live up to their future ambitions, a study on “moral forecasting” found that people behaved more honestly than they predicted. Since this interesting prediction error has only been identified in a few studies and its direction may seem surprising, psychological science could benefit from a high-powered replication. In Experiment 1, we will conduct a close replication using the original cheating task and a general population sample from the same country as the original study (Canada). By extension, we will also include the “mind-game paradigm” as an established deception-free cheating task to assess task generalizability. If the primary hypothesis is supported, then we propose a second extension in Experiment 2, by examining whether a cognitive debiasing intervention can reduce the moral forecasting error in a general population sample from the US. As a final extension we will also examine the social dimension of lay beliefs, by assessing whether moral forecasts for other people exhibit the same prediction error as moral forecasts for oneself. In the current experiments, the planned sample size will provide 90% power to detect ¼ of the observed effect size from the original study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.785 | 0.991 |
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