Executive CompensationNational Library of Canada Cataloguing in Publication
Bibliographic record
Abstract
Each "20 Questions " publication is designed to be a concise, easy-to-read introduction to an issue of importance to directors.The question format reflects the oversight role of directors which includes asking management – and themselves – tough questions. The questions are not intended to be a precise checklist, but rather a way to provide insight and stimulate discussion on important topics. In some cases, Boards will not want to ask the questions directly but they may wish to ask management to prepare briefings that address the points raised by the questions. The comments that accompany the questions provide directors with a basis for critically assessing the answers they get and digging deeper if necessary.The comments summarize current thinking on the issues and the practices of leading organizations.They may not be the best answer for every organization. Thus, although the questions apply to any organization, the answers will vary according to the size, complexity and sophistication of each individual organization.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.705 | 0.674 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".