Bibliographic record
Abstract
It is a great honor and pleasure to have this opportunity among so many friends to discuss issues that have become increasingly central to our profession. It is a testament to our partial progress towards gender equality that Conference organizers believed that these issues were sufficiently important to showcase in a keynote address. Such topics rarely received even a walk-on role when I entered the profession. I graduated from law school in the late 1970s without having a single course by or about women. There were no women’s law associations, and I saw no women partners when I was interviewing for jobs. What is most striking to me now is how little of it was striking to me then. Most of us did not perceive the absence of women or women’s issues as a problem. It was just how law, and life, were. Today, the legal landscape has been transformed. But we still have a version of what I have called the “‘no problem’ problem.” Women’s increasing representation and visibility in the profession is taken as evidence that “the woman problem” has been solved. A widespread assumption is that barriers have been coming down, women have been moving up, and it is only a matter of time before full equality becomes an accomplished fact. In a recent survey by the ABA Journal, only a quarter of female lawyers and three percent of male lawyers thought that
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 | 0.025 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.058 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".