Examining the Websites of Canada’s ‘Top Sex Crime Lawyers’: The Ethical Parameters of Online Commercial Expression by the Criminal Defence Bar
Bibliographic record
Abstract
Online advertising has become a primary source of information about legal services. This trend towards web-based marketing of legal services poses new challenges to the regulation of the legal profession. Challenges which, to date, have not been fully met. It also creates a new source of data for researchers studying aspects of the legal profession such as legal ethics, lawyers’ perspectives and strategies, and legal discourse. The objective of this study is to examine the most prominent websites in Canada that advertise legal representation for individuals accused of sexual offences. The study of these websites yielded two types of observations regarding the commercial expression engaged in by this subset of the criminal defence bar. The first pertains to the parameters of ethical advertising by criminal defence lawyers who practice sexual assault law. A significant subset of lawyers who advertise legal representation services to individuals accused of sexual offences engage in commercial expression that may be inconsistent with the limits and guidelines specified in their professional codes of conduct. The study produced a second observation. Examination of these websites offers a window into the narratives about sexual assault that some defence lawyers construct for their clients or the public, and perhaps also the perspectives about sexual assault held by some defence lawyers themselves.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".