Diversity in the Boardroom: A Content Analysis of Corporate Proxy Disclosures
Bibliographic record
Abstract
My work in this field has focused on regulation by quota and regulation by disclosure. With regard to quotas, strikingly, the Norwegian law is not located in regulation that explicitly deals with human rights or equality issues; rather, it is found in the heart of the legal regime that gives life and personality to corporations – in Norwegian corporate law. I have conducted qualitative, interview-based research with Norwegian corporate directors, both men and women. It is only through understanding how the goals of the law have translated into the day-to-day existence of these individuals that we can begin to consider the “big picture” questions that accompany the quota-based approach. With regard to disclosure, I have chosen to focus on the U.S. as a second case study for four principal reasons. First, similar to the Norwegian law, the site that houses the U.S. rule is noteworthy. Once again, it is not found in regulation that focuses on anti-discrimination etc…; rather, it is located in the heart of the legal regime that governs the public issuance of shares – in U.S. securities law. Second, and related to the first, the U.S. rule (like the Norwegian law) has been controversial, painted by some as an unjustified intervention into market terrain and as being in tension with the underlying purpose of securities regulation. Third, quite simply, U.S. markets represent the biggest share of overall global market capitalization. Fourth, I am mindful of the argument of scholars such as Schuck that there is something special – something unique –about the U.S.’s historical engagement with the idea of diversity. My inquiry into the U.S. approach, using the diversity disclosure rule promulgated by the SEC, begins with an overview of its conceptual underpinnings. I then explore reactions to the rule and consider whether, in promulgating it, the SEC acted reasonably, or if it strayed significantly from its mandate. From there, I use a mixed-method, qualitative–quantitative content analysis to investigate the micro-dynamics of this approach. I take an initial temperature reading of corporate articulations of diversity under the first years of the rule. These articulations are particularly fascinating given that the SEC does not provide firms with a definition of the term “diversity”. The specific results of my study are forthcoming. Overall, it establishes that the concept of diversity carries multiple connotations for U.S. corporations. However, perhaps its most salient finding is that, when left to their own devices (i.e. in the absence of regulatory guidance), firms most frequently think in experiential terms and focus on a director’s prior experience, or knowledge and skills — rather than in socio-demographic terms with an eye to gender or racial diversity. As I have reported elsewhere, only approximately half of firms in my sample fell into the latter camp.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".