Getting Back to Basics: Challenging Complexity and Accountability in the Boardroom
Bibliographic record
Abstract
This paper investigates the dynamics of complexity and expertise in the context of compensation committees (ccs). Drawing on semi-structured interviews, mostly with cc members and consultants, we bring to light two axes of subordination that impact the mindset of corporate governance participants, and may ultimately undermine directors’ degree of accountability to shareholders. The first axis involves cc members’ subordination to consultant expertise, which tends to be considered as an indispensable ally in dealing appropriately with the webs of complexity that allegedly characterize executive compensation. Nourished partially by the first axis, the second implies subservience to these webs of complexity, which are widely presumed and naturalized by cc members and the consulting experts they employ. One of our main contributory statements is to question the ascendancy of complexity in the boardroom, casting doubt on one of the key assumptions upon which practices and expertise in contemporary corporate governance institutions are built and promoted. We also question the extent of epistemic dependency in many compensation committees, where much of the knowledge necessary to properly operate the repertoire of practices (deemed necessary to address the problem of executive compensation determination) is not primarily in the hands of cc members, but rather in those of consultants.
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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 | 0.024 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.014 | 0.058 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".