Bibliographic record
Abstract
In this chapter I examine the concept of elder abuse, arguing that this extends beyond the more limited notion of the criminal victimisation of the elderly. Drawing on examples of research studies and legislation from the UK, Canada and the USA the principal argument of the chapter is that the traditionally positivistic methods adopted by criminologists to count and otherwise understand crime (mainly in the form of victimisation surveys and police data) underestimate greatly the prevalence of elder victimisation, particularly when such victimisation is understood to encompass broader 'social harms' not necessarily recognised as official 'crimes' by the criminal law and in any case not often coming to the attention of the criminal justice system. In adopting a broad approach to the questions of social harms befalling older people, this paper of course reflects the primary arguments of the so called critical schools of criminology and victimology, which hold that criminologists and victimologists have for most of their history focused the majority of their attention on those notions of crime and criminal justice espoused by states (McBarnet, 1983)
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".