Bibliographic record
Abstract
When asked to make this presentation, as every good scientist (I also started a PhD in bio-chemistry), I said to myself: let's do a bit of research.Who are and who have been female lawyers of Italian origin practising in the province of Québec.To my dismay I discovered that I am probably one of the deans, the godmother in a sense.I have not discovered anyone who is in active private practice in the province of Québec who has been a member of the bar for a period longer than myself.The first woman of Italian origin who was admitted to the bar was Rosa Gualtieri.Rosa Gualtieri was born in Québec, the daughter of an Italian Protestant minister.She graduated as the only woman in her class from the McGill law faculty in 1951 and became a member of the Bar in 1952.Rosa practiced family law as a sole practitioner for most of her life.She died about two years ago.I never met her.Interestingly, when I enquired about her in the community no one could tell me anything.I also asked the Congress of Italian Canadians and the Canadian-Italian Business and Professional Association (CIBPA) but no one knew her.It was only members of the Jewish community, Mr. Martin Franklin and Mr. Joe Mendelsohn, who remembered her.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".