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
Decades of research has argued that social information processing can improve belief accuracy as measured at both the group level and the individual level. However, we show both theoretically and empirically that this effect is limited to numeric estimates. In discrete choice estimates, also known as classification tasks–such as yes/no decisions, or selecting the better of two options–social influence simply amplifies the majority opinion, regardless of the accuracy of that opinion. As a result, initially inaccurate groups become less accurate but more confident. This effect is not due only to the type of information exchanged, but applies in more generally any case where group members are polled on a discrete choice, as in a voting process. These results point to the need for a contingency theory of collective intelligence identifying the types of decisions for which social information processing can improve outcomes. In the case of estimation accuracy, these results also point to a simple but effective strategy: organizations should focus on aggregating beliefs about numeric quantities, and avoid framing problems as a discrete choice until as late as possible in the decision process.
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 | 0.001 | 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 | 0.001 |
| 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