Bibliographic record
Abstract
Work on this text began after our empirical research projects, named below, had been completed and has been ongoing for a number of years.However, the ideas about the mistreatment of older people developed in the book are grounded in those earlier studies.Our great thanks go to all the participants who shared with us their experiences of trying to understand and respond to mistreated, neglected, and self-neglecting older people.Some were dedicated professionals and some were dedicated volunteers, including many in later life who maintained a commitment to provide assistance to their peers.Their struggles to "do the right thing" while respecting older people's wishes encouraged us to explore further why this is such a difficult task and were instructive in shaping our approach to this book.We also owe a debt to our other professional and scholarly colleagues in Canada and the United Kingdom whose willingness to discuss their own work and ideas assisted us in developing ours.We especially wish to thank our referees who offered valuable feedback and suggestions for our work.We express our deep appreciation for the work of the late Anne Martell.She was both research coordinator and chief interviewer for our second and third studies.Anne brought research expertise, knowledge, and wisdom to the research as well as a commitment to better the lives of older people.These characteristics, combined with her high level of respect for participants, allowed us to access many people who might otherwise have
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.285 | 0.176 |
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".