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 argue that various types of evaluative social judgments about the self or others (e.g., employee job performance ratings, self-reported attitudes, ratings of others' traits) may be obtained more accurately using comparative ratings rather than absolute ratings. Comparative ratings involve relative judgments of a target in comparison with other individuals or groups, whereas absolute ratings involve judgments of a target on scales that do not explicitly reference other people. In industrial-organizational, social, and personality psychology research that has compared the validity of comparative and absolute ratings, we have found evidence of more valid measurement as a result of comparative judgmental ratings, despite the nearly exclusive reliance on absolute judgmental ratings in these areas. We offer a social cognitive and evolutionary explanation in support of the hypothesis that humans may often be able to make more accurate ratings using comparative measures. We also recommend an agenda for greater exploitation and understanding of relative judgments in psychological research and practice.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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