INTRODUCTION TO SPECIAL ISSUE: UNDERSTANDING AND RESPONDING TO PAIN-BASED BEHAVIOUR IN CHILD AND YOUTH CARE WORK
Bibliographic record
Abstract
We are grateful to Dr. Sibylle Artz, Editor of the International Journal of Child, Youth and Family Studies, for the invitation to produce a special issue on pain-based behaviour in child and youth care work. Since the term was created and published in 2002 (Anglin, 2002), the notion has entered the literature, the research, and, perhaps most importantly, the practice of child and youth care internationally. The eight articles in this issue come from Ireland, Australia, the United States, and Canada, and offer a broad range of perspectives.After receiving the invitation to compile this issue, we scanned the recent child and youth care literature and readily identified 13 publications — articles and books — using the term pain-based behaviour. There are undoubtedly many more, however we believed the authors of these publications would present a significant cross-section of perspectives on understanding and responding to pain and pain-based behaviour. We are excited and honoured that the authors represented here were able to contribute articles to this issue.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".