Bibliographic record
Abstract
Ryan Trainer (RT):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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".