Devalued Liberty and Undue Deference: The Tort of False Imprisonment and the Law of Solitary Confinement
Bibliographic record
Abstract
Despite numerous calls for reform and restraint, solitary confinement continues to be both misused and overused in Canadian prisons. This paper charts a path through which to address such misuse, but analyzing solitary confinement through the tort of false imprisonment. This analysis is new: while some scholars have examined how other branches of tort law can address harms caused by solitary confinement, none have examined the application of this tort. I argue that the tort of false imprisonment provides segregated prisoners with an effective means through which to seek compensation for individual harm. As an intentional tort that is actionable per se, the tort not impose onerous evidentiary burdens on plaintiffs. Rather, the heavy lifting must be done by government: once the plaintiff proves complete confinement, it falls on prison authorities to demonstrate that the confinement was legally justified. This evidentiary distribution is well-suited to address the profound imbalance of power in the prison setting. Moreover, since the tort of false imprisonment is designed to prevent unwarranted intrusions on liberty, dignity, and personal autonomy, it can effectively respond to the harms that are typically suffered in segregation. The tort allows prisoners to bring individualized evidence of harm, and to seek remedies for both tangible and intangible losses. If substantial awards are issued, the financial burden might compel much needed change in culture and daily management of segregation. Despite this promise, the tort’s progressive potential has yet to be realized. To date, the courts have shown significant deference to the discretionary authority of prison officials, even in the face of evidence that such authority was improperly exercised. In addition, even in successful cases where unlawful segregation is found, the courts have issued only paltry general damage awards – generally set at $10 per day – on the rationale that a violation of a prisoner’s liberty interests is simply not worth as much as that of the free. This approach is problematic not only for its failure to appreciate the profound harm caused by segregation, but also, for its unprincipled departure from application of the tort in cases involving the unincarcerated.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.033 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".