Bibliographic record
Abstract
To better understand how using a novel in a child and youth care classroom impacts empathy in relation to gender diversity, a qualitative study was constructed. Data were gathered using an online questionnaire administered to child and youth care practitioner students. These students had engaged with the novel Scarborough (Hernandez, C. [2017]. Scarborough: A novel. Arsenal Pulp) in a course about foundational therapeutic knowledge. The study sought to identify: (a) what perceptions and emotions were evoked by engaging with the narrative of a young person exploring gender; (b) what, if any, aspects of empathetic connection emerged in relation to this exploration; and (c) what, if any, connections were made to the theoretical material taught in the course. The study incorporated child-and-youth-care-specific and critical social theory frameworks, and theorized about evocative objects and the concept of empathetic distress. The findings suggest that novel-based teaching can elicit from students, or help them express, higher-order empathy in relation to gender diversity, and that a narrative about the struggle to live as one’s genuine self is one possible pathway towards achieving this empathetic connection. Additional research is needed to investigate these preliminary findings and to address bias in the existing literature on adult education and the use of fiction.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".