Bibliographic record
Abstract
This chapter commences with an interview with Christiane Bergevin, Executive Vice President at Desjardins Group. In Canada there are women at the top, and their thinking process is slightly different, and it is highly complementary with men. According to her, quite often women will be more intuitive, will be very good at thinking out of the box and, very importantly, also very good at reading personalities. Women have to network a lot more and a lot earlier, act as if there were no barriers. Women each have a role to play and a large number of men are also ready to do so. In Quebec, there are more women CEOs than anywhere else in the country, because there has been a program of equal representation on boards that are owned by the Government, such as the Liquor Board. The governor of Quebec has invested in creating equal access for women.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.010 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".