Tuning into child and youth care: an audio drama inquiry with child and youth care practitioners who have lived in residential placement
Bibliographic record
Abstract
Child and youth care (CYC) practitioners (CYCPs) who have lived in residential placement as children or youth represent an understudied and thus largely unknown cohort. This lack of knowledge has resulted in assumptions, generalizations, and unfounded claims impacting discourses, and potentially practices, within CYC. Based on the development of an original research method—audio drama inquiry—this sonic dissertation presents the first documented examination of the perspectives, experiences, and insights of 17 Canadian CYCPs “from care” (CYCPfC). Informed by research-based theatre, CYC theory, and care ethics, two audio drama series were created asking “what does residential placement experience do to CYCPs, and how do CYCPfC do CYC?” The resulting performances reveal frictions and desires related to working for, within, and at times against the same systems that one grew up in. CYCPfC articulate benefits resulting from their “lived experience,” such as identification, empathy, inimitable systemic knowledge, and motivations to initiate change within such systems. However, the audio dramas also reveal perils related to their personal histories, the institutions in which they work(ed), and the “the field” more broadly. Through greater understanding of CYCPfC, who provide insights, cautions, and learnings from their unique perspectives, this study advances our knowledge regarding what is done when doing CYC. Moreover, Tuning into CYC broadens existing frames of qualitative inquiry through explicating and demonstrating the theoretical and practical elements of audio drama inquiry.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.025 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".