Helping criminal justice system users: utilising specially trained dogs
Bibliographic record
Abstract
It has been shown that dogs can have numerous beneficial effects on individuals, for example, being able to alleviate stress (Aydin, et al., 2012). Countries such as USA, Portugal and Canada, have expanded such use of dogs and use specially trained courthouse dogs to accompany witnesses whilst testifying but also, for example, during medical examinations (Sandoval, 2012). Recently, in England and Wales, specially trained dogs have been introduced into the Criminal Justice System. However, there is currently no evidence evaluating such initiatives worldwide and most information on the effects a specially trained dog can have on individuals is anecdotal. The aim of this talk is to present current knowledge on using specially trained dogs within the Criminal Justice System. It is also to provide preliminary results from a selection of our studies exploring the use of specially trained dogs as viewed by the public and as viewed by court users who were offered this service. Preliminary results show benefits of using specially trained dogs within the Criminal Justice System when approached with care and when dog is appropriate/appropriately trained.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".