Appendix S1 - Measuring the Distribution of Spitefulness
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
Spiteful, antisocial behavior undermines the moral and institutional fabric of society, producingdisorder, fear and mistrust. Previous research demonstrates the willingness of individuals to harmothers, but little is understood about how far people are willing to go in being spiteful or theirconsistency in spitefulness across repeated trials. Our experiment is the first to provideindividuals with repeated opportunities to spitefully harm anonymous others when the decisionentails zero cost to the spiter and cannot be observed by the object of spite. This method revealsthat the majority of individuals exhibit consistent (non-)spitefulness over time and that thedistribution of spitefulness is bipolar: when choosing whether to be spiteful, most individualseither avoid spite altogether or impose the maximum possible harm on their unwitting victims.
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 | 0.003 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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