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 Evil in Mind: The Psychology of Harming Others offers readers an accessible, social-scientific understanding of the concept of evil and its various incarnations. Rather than simply using “evil” as an undefined synonym for human nastiness, Part 1 of the book first establishes when and why people apply the “evil” label to perpetrators and their misdeeds. It also addresses why most people do not want to see themselves—or be seen by others—as evil: Being labeled “evil” is the ultimate signifier of social rejection. Indeed, although dogged pursuit of good feelings and the effortful avoidance of bad feelings often causes suffering for others, people make use of an astounding array of cognitive reframing and self-presentation strategies to dodge the “evil” label. Part 2 illustrates how these core principles can aid comprehension of phenomena such as hate, sadism, serial killers, and group-based evil such as genocide, corporate wrongdoing, and familial abuse. Throughout, Evil in Mind attempts to nudge the reader toward a mindset that is self-reflective rather than ghoulish or self-congratulatory: Whether one’s actions result in harm that is horrifically irreparable or comparatively minor, the motives driving such actions and the menu of goals and strategies for deflecting condemnation are not really all that different. Thus, Evil in Mind presents the reader with a systematic, research-based psychological analysis of the phenomenon of evil that is compact, digestible, and potentially transformative.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.463 | 0.001 |
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