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
Although prejudice-reduction policies and interventions abound, is it possible that some of them result in the precise opposite of their intended effect--an increase in prejudice? We examined this question by exploring the impact of motivation-based prejudice-reduction interventions and assessing whether certain popular practices might in fact increase prejudice. In two experiments, participants received detailed information on, or were primed with, the goal of prejudice reduction; the information and primes either encouraged autonomous motivation to regulate prejudice or emphasized the societal requirement to control prejudice. Ironically, motivating people to reduce prejudice by emphasizing external control produced more explicit and implicit prejudice than did not intervening at all. Conversely, participants in whom autonomous motivation to regulate prejudice was induced displayed less explicit and implicit prejudice compared with no-treatment control participants. We outline strategies for effectively reducing prejudice and discuss the detrimental consequences of enforcing antiprejudice standards.
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 | 0.000 | 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) | 0.001 | 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