Anger Management: An Anger Management Training Package for Individuals with Disabilities.
Bibliographic record
Abstract
I have known Dr. Lenore Terr professionally and personally for many years. Mostly she has written and presented on Trauma. This book “Magic Moments of Change” shows a different side: the consummate clinician. Dr. Terr trained in Ann Arbor, Michigan, and this book is her and others’ way of honouring her mentor, Saul Harrison who died in 2004. She approached a number of child and adolescent psychiatrists, both American and Canadian, to submit vignettes that were pivotal in leading to change in their patients, and to briefly explain how these moments worked. Thirty-three responded with 48 case presentations. She also included moments from the long-term treatment of a “wild child” that she worked with successfully over fifteen years and which serves to pull the book together, giving continuity and illustrating the many points she wishes to make. Her goal was to shed light on the art and science of psychotherapy, focusing on individual therapy and what leads to change on “how psychotherapy turns kids around” (title).
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.011 |
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".