Sense-Making in Compensation Committees: A Cultural Theory Perspective
Bibliographic record
Abstract
Drawing on Mary Douglas’s cultural theory, our research analyzes the cultural schemes or biases mobilized by compensation committee (CC) members, in the context of public companies, when making sense of their committee’s work. Relying on semi-structured interviews mostly conducted with CC members in Canada, our analysis brings to the fore the production of moral and rational comfort within the boundaries of the individualistic and hierarchical culture. Under an individualistic bias, the compensation market is seen as natural, providing conditions of possibility that serve to establish fair compensation through the creation and enforcement of contracts. Under a hierarchic bias which emphasizes principles of objectivity and measurability, members of CCs tend to conceive the design of compensation policies as an act of expertise, relying extensively on consultants and measurement techniques to determine acceptable reward boundaries. Not only does our paper contribute to corporate governance literature by providing insight into a central aspect of CCs, that is to say CC members’ ways of thinking and doing, but the juxtaposition of cultural theory with CC empirics provides us with the opportunity to reflect and theorize on the issue of cultural change.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.064 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".