Bibliographic record
Abstract
When I read this book a few years ago, when it was first published, I was so excited about it that I knew I wanted to review it to help others become aware of it. Regretfully, I did not manage to complete the review then. As I have reread the book in order to review it now, I am just as excited and hopeful that my review will encourage others to read the book. Experiencing race as a music therapist: Personal narratives consists of narratives—stories—collected by Susan Hadley through interviews with 17 music therapists. She says, “It is a collection of diverse and complex narratives by some of the people in our profession who are grappling with these issues” (p. 14). The narratives are (from Hadley’s description) from a female of mixed Native American and European heritage, a Jewish Canadian male, two white South African females, a male of mixed Torres Strait Islander and European heritage, two white Australian females, a Maori male, a Caucasian English female now living in New Zealand, two African-American females, a Caucasian American female, a Korean female living in the US, a Japanese person living in the US, a Caucasian American male, an Iranian American female of Jewish and Muslim parentage, and a white Puerto Rican female living in the US. (p. 14)
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.112 | 0.001 |
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 teacher head, 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".