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
Abstract. We investigated the pleasurability of aggressive behavioral decisions. Four questionnaires (on hedonicity, decision making, justification of aggression, and impulsiveness) were given to 50 participants of both sexes, ranging from 16 to 80 years old. Most participants avoided unpleasant behaviors as part of a trend to maximize pleasure and to minimize displeasure. Mean hedonicity ratings followed a bell curve with increasing levels of aggressiveness (p < .0001). Thus, the participants chose neither passive nor highly aggressive responses to social conflicts, with both extremes receiving the most unpleasant ratings. The results offer empirical support for an interesting point: People may derive pleasure from aggression as long as it is exhibited on a low to medium level. More precisely, people associate pleasure with aggression up to a certain point: Aggressive responses of medium intensity were rated significantly less unpleasant than the most passive and most aggressive ones, which were associated with less pleasure. Conclusion: In social conflicts, behavior tends to maximize experienced pleasure; and aggression produces pleasure in the aggressor, except at extreme intensities. The point that mild to moderate aggression brings pleasure, whereas extreme or severe aggression does not, provides a perspective that may reconcile conflicting observations in the literature.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
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