Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When lawyers elect the leaders of their self-regulatory organizations, what sort of people do they vote for? How do the selection processes for elite lawyer sub-groups affect the diversity and efficacy of those groups? This article quantitatively assesses the demographic and professional diversity of leadership in the Law Society of Upper Canada.\nAfter many years of underrepresentation, in 2015 visible minority members and women were elected in numbers proportionate to their shares of Ontario lawyers. Regression analysis suggests that being non-white was not a disadvantage in the 2015 election, and being female actually conferred an advantage in attracting lawyersâ votes. The diverse employment contexts of the provinceâs lawyers were also represented in the elected group. However, early-career lawyers were completely unrepresented. This is largely a consequence of electoral system design choices, and can be remedied through the implementation of career-stage constituencies.\nThe Law Society's "benchers" are more demographically diverse than other elite lawyer sub-groups such as judges, and the open and transparent selection process may be part of the reason.
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.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it