Bibliographic record
Abstract
This article integrates story and the form of qualitative methodology known as arts-based inquiry. The authors use this approach to provide a case study of Kal, a 15-year-old boy who had unsuccessfully attempted to end his life by “flying” off his apartment balcony. The paper begins with orientation to the background of this case and to arts-based inquiry and case history and then proceeds with an imaginative re-creation of the involvement of Margie, Kal’s caregiver, in this case in the form of a letter written in role by her as Kal to his mother. Finally, the authors discuss how arts-based representations can be used to positively affect mental health and to generate creative healing energy. In this presentation Kal is the leading character, and Margie, Kal’s real-life teacher in a hospital-based mental health unit in Ontario, Canada, is the supporting actress. Through dramatic fictionalization of her work with Kal, Margie found that “human learning can be renewed when teacher researchers use arts-based textual strategies to reflect on experience and invite others to respond to these inquiries” (Diamond & Mullen, 1999, p. 18).
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.523 | 0.154 |
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".