Bibliographic record
Abstract
To start, I am going to try to jog your memory a little bit.You studied law at King's College, University of London; why did you decide to study law?Janet Baldwin (JB): In those days, law in England was and still is a first degree, which meant I was very young when I was going to law school and when I started teaching.There was no such thing as career counselling, so it was not that anyone guided me towards law.Certainly it was not in the family.I was interested in debating, and logic, and I thought law would be interesting.In England, unlike here, law was not necessarily seen as a route to practice or not only seen as such.It is in a sense a general formation.I had other interests but many of them seemed less practical, such as linguistics.There were not many women in law school at that time, either as students or faculty, but there were some.I think things had changed on that front earlier in England than in Canada because of the war, with women entering professions that were previously perceived as male occupations.Although there were not many of us, there was a group of us.RT: Since you have agreed to let me test your memory, do you remember how many women were in your class, even a rough estimate? Interview conducted by Jessica Davenport and Ryan Trainer.Janet Baldwin graduated with an LL.B from the University of London King's College in 1964 and an LL.M from the University of Illinois, after attending the
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.021 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".