Bibliographic record
Abstract
This paper discusses common problems evident in many governance reviews with particular emphasis on the measurement problems often evident in such undertakings. The paper is written from the perspective of a consultant who has had the opportunity to assess the governance practices of numerous corporate and not‐for‐profit organizations and is based on the author’s experiences and anecdotal perspectives, as well as on the guidance available from comparing frequently observed practices in the measurement of governance matters with related instances of workplace assessment. Common measurement problems are outlined, such as unpegged rating scales, poorly crafted 360E reviews, self‐assessment, confusion between outcome and process measures and the lack of normative standards, with a particular emphasis on understanding limitations in the utility of such reviews imposed by the quality of the data. Methods to improve the quality of governance review information are presented, together with a practical framework to implement a process of governance review at the board table.
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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.499 | 0.542 |
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".