Bibliographic record
Abstract
The last decade has witnessed an exponential growth in user-generated online content featuring Non-Suicidal Self-Injury (NSSI), including photography, digital video, poems, blogging, and drawings. Although the increasing visibility of NSSI content has evoked public concern over potential health risks, little research has investigated why people are drawn to create and publish such content. This article reports the findings from a qualitative analysis of online interviews with 17 individuals who produce NSSI content. A thematic analysis of participants' narratives identified two prominent motives: self-oriented motivation (to express self and creativity, to reflect on NSSI experience, to mitigate self-destructive urges) and social motivation (to support similar others, to seek out peers, to raise social awareness). Participants also reported a double-edged impact of NSSI content both as a trigger and a deterrent to NSSI.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".