Bibliographic record
Abstract
Ten years ago, the first edition of this book gently surprised the clinical community. It was the first single volume to present mental health perspectives of diverse subcomponents of the deaf community. Clinicians who read that volume reorganized their understanding of deaf people as a singular homogeneous group and began to appreciate the wide diversity within and between groups who are deaf. It was perhaps the first formal nudge to conceptualize the word “deaf” as belonging to different cultures, different groups, and different conditions. As editor, Irene Leigh, through her exemplary work, has now raised the bar of excellence (which should be our clinical standard) even higher. In this revised and updated second edition, readers are no longer nudged but are startled into reconceptualizing, reorganizing, and, in some instances, learning for the first time about the subtle and not-so-subtle subcomponents of the deaf community. The degree to which one feels somewhat startled is perhaps reflective of the fact that psychotherapy with the deaf community has substantially lagged behind delivery of service to other groups and also that there are still relatively few of us out there doing it. As the first (and so far the only) deaf clinical psychologist in Canada, I was both energized by this book and ashamed by my lack of knowledge regarding diversity. Some of it generates a “how did I not know that?” reaction, whereas other details represent information that I have not been exposed to or perhaps never sought information about before.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".