Bibliographic record
Abstract
Elder abuse is a notoriously difficult crime to prevent or solve. Elder abuse can be psychological, physical, emotional, sexual or financiali and many victims of elder abuse suffer multiple types of abuse simultaneously,ii often at the hands of loved ones, which makes it difficult to detect, report, investigate or prosecute. There is no single solution to ending elder abuse. In March 2011, prompted by media reports of a grandmother forced to live in the family’s garage through a Toronto winter, CARP called on then Minister for Seniors, Julian Fantino to do more to end the scourge of elder abuse and specifically asked for the increased sentencing for elder abuse convictions that was featured in the government’s 2011 election platform. Bill C-36, An Act to Amend the Criminal Code (Elder Abuse) was introduced in April 2012 as a part of the federal government’s commitment to combating elder abuse. On its own, Bill C-36 cannot end elder abuse, but it is an important tool in deterring and prosecuting criminal cases of elder abuse. CARP expects elder abuse to remain a priority for the federal government and encourages the expedited passage of Bill C-36. Multiple levels of government and broad sections of society must all play roles in preventing, detecting, investigating, and prosecuting elder abuse. The federal government, for its part, is in the sole position of
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.747 | 0.653 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".