Functional Stupidity in the Boardroom
Bibliographic record
Abstract
This study provides a better understanding of the dynamics of knowledge and expertise in the context of public companies’ compensation committees (CCs), through a focus on CC members’ cognitive limitations. Drawing on semi-structured interviews, we mobilize the concepts of reflexive thinking and functional stupidity (Alvesson and Spicer 2012) to document and analyze CC members’ difficulties and/or unwillingness “to use cognitive and reflective capacities in anything other than narrow and circumspect ways” (Alvesson and Spicer, 2012: 1201). Overall, our findings indicate that CC members, although being firmly committed to knowledge development and problem solving, are disinclined to mobilize three key aspects of cognitive capacity (i.e., reflexivity, meaningful justification and substantive reasoning) in the design of remuneration policies. Our study also shows that these cognitive limitations are fuelled by a multidimensional exercise of power, which we conceive of as a form of “stupidity management” (Alvesson and Spicer 2012). The latter aims to limit CC members’ meaningful communicative action by constraining disruptive thinking and preventing critical issues from impacting committees’ agenda and deliberations – all of this in the name of aspirational yet superficial forms of decision-making leadership in the boardroom. Our analysis also highlights the central role of compensation consultants as “stupidity managers”, involved in the orchestration of the constraining of committee members’ mind. Significant implications of these findings for research and policy-making are discussed.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".