Understanding “Elder Abuse and Neglect”: A Critique of Assumptions Underpinning Responses to the Mistreatment and Neglect of Older People
Bibliographic record
Abstract
This article provides an overview of the ways in which the mistreatment and neglect of older people have come to be understood as a social problem, one which is underpinned by a variety of substantive and theoretical assumptions. It connects the process of conceptualizing elder abuse and neglect to political-economic and social evolution. The authors draw on a review of the literature, government sources, interest group websites, and their own research to provide a critical commentary illustrating how these understandings have become manifest in legislation, policies, and programs pertaining to "elder abuse and neglect" in Canada. Suggestions are provided for changes in direction for policies, programs, and research.
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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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.006 | 0.052 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".