Innocence Compensation: The Success Rate of Actions for Negligent Investigation
Bibliographic record
Abstract
This research is a qualitative and quantitative analysis on the success rate for plaintiffs who bring actions for negligent investigation against police officers who have wrongly accused them of criminality. This research falls within the broader framework of the success rate of plaintiffs who also seek damages from both police services and crown counsel regarding actions seeking damages for malicious prosecutions, breaches of rights under the Canadian Charter of Rights and Freedoms, and findings of miscarriages of justice pursuant to section 696.1 of the Criminal Code of Canada. The data analysis shows that on a national basis, plaintiffs have a better than one in four chance to succeed in an action claiming damages for the tort of negligent investigation including its comparative remedy at civil law. The case law analysis shows that this tort has been proven when investigations have included relatively benign activity such as a simple mistake up to and including the characterization of police activity as reprehensible and high-handed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".