Bibliographic record
Abstract
Family law has always had a reputation for being soft law, the area that lady lawyers practice, and a pink ghetto. Family law is not easy. There are upwards of 70 pieces of family law related legislation across Canada, to say nothing of the rules of court and process related legislation and skills, financial complexities, and family violence concerns. When no-fault divorce was introduced in 1968, lawyers did not specialize in family law. According to Constance Backhouse “most male lawyers eschewed divorce as odious, describing it as more ‘social work’ than ‘real law,’ and expressing reluctance to represent female clients whom they deemed overly emotional.” By 1970, only 313 women had been admitted to the bar in Ontario. Despite the increasing number of women who needed lawyers, there were few who were willing to practice family law. Women like Justice Claire L’Heureux-Dubé stepped in and developed the practice of family law. However, family law continues to suffer from a reputation for being not real law and something that lawyers can easily dabble in. As Robin West has suggested, “the more women in a field, the less prestigious” (at 979). In this column, I look at a recent negligence case that serves as a reminder not to practice family law without the requisite expertise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.017 | 0.031 |
| Insufficient payload (model declined to judge) | 0.006 | 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".