Bibliographic record
Abstract
Although self-injury seems to be a current hot topic in the media, little research has been conducted on the possible addictive nature of self-injurious behaviors. Faye (1995) found that self-injury has a number of characteristics that have also been identified in the behaviors of drug and alcohol addicts, including similarities in origin in emotions experienced, in family structure, and in the repetitive nature of tension-release responses (Favazza & Conterio, 1988; Faye, 1995). Derouin and Bravender (2004) found that self-injury has an addictive quality, because many self-injurers develop an overwhelming preoccupation with the relief experienced after an episode of self-injury. Sharing many of the same addictive qualities those who suffer from chemical dependency, self-injurers often continue to harm themselves despite negative consequences or the knowledge that self-injury is indeed a problem. The fact that many self-injurers report feeling like they are addicted to harming themselves lends more evidence to the idea. Using an adapted version of the Ottawa/Queen's Self-Mutilation Questionnaire, the results found significance in eleven of the fifteen hypotheses put forth at the beginning of the study, The results also suggested that many respondents appeared to have built a tolerance to the effects of their self-injuring behaviors, and they experienced withdrawal symptoms if they stopped self-injuring for a period of time. These results lend credit to the idea that self-injury is, in fact, related to addiction.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".