Is the Canadian legal profession at risk? An analysis of lawyer victimization across Canada.
Bibliographic record
Abstract
This dissertation discusses lawyer victimization across Canadian provinces and territories, a research project that employed both quantitative and qualitative methods, utilizing an exploratory survey approach to canvass 15,746 practicing lawyers and undertaking 61 lawyer interviews.Findings from the survey revealed that threats ranged from inappropriate communications and approaches to explicit threats to harm including physical assaults and death threats.Robust findings included gender differences with regard to reactions to aggression, and that occupation, not gender, is relevant to receipt of aggression.Theoretical discussions were triangulated to also include the author's 2006 public opinion survey of lawyers, canvassing the general public (n=182) and university students in a large Western Canadian university (n=480).In the lawyer interviews, numerous themes were explored -theoretical assumptions; gender issues in practicing law, self-represented individuals in the legal system; the public's access to legal knowledge online; and unethical billing practices.As well, possible solutions were proffered: promoting legal literacy in elementary/secondary schools; transitioning law school academics to legal practitioners; enhancing law firm mentorship programs, and bringing awareness of lawyer victimization to the provincial bar societies and the Canadian Bar Association.Unless coordinated efforts are undertaken to address aggression against lawyers, legal practitioners, especially women, will continue to suffer victimization and severe psychological repercussions.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.025 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".