Bibliographic record
Abstract
Abstract Self-injury, the purposeful damaging of one’s own body tissue without suicidal intent represents a common and significant mental health concern across the globe. Notwithstanding the major strides made in our understanding of self-injury over the past twenty years, it remains enveloped by substantial stigma and misunderstanding. Unfortunately, this foments profound shame, isolation, and hopelessness for many people with lived experience of self-injury. As such, how we think about self-injury and support people who self-injure requires an approach grounded in understanding people’s unique stories and experiences. This involves explicit recognition of the many strengths and underlying resilience that all people with lived experience possess. Hence, this book is the first to offer a person-centered understanding of self-injury, with particular emphasis on several areas often overlooked in the literature, including: stigma, the language used to talk about self-injury, and recovery, among many others. Given its applied nature, the content of this book has relevance for mental health professionals and trainees, school professionals, families, and researchers. Moreover, by virtue of applying the content in this book, readers will not only develop a deeper and more compassionate understanding of self-injury but will be ideally positioned to work appropriately and effectively with individuals who have lived experience of self-injury.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".