A brokerage-based measure of organizational diversity and exploratory analysis of regulatory violations among Fortune 100 corporations
Bibliographic record
Abstract
Abstract In this paper, we introduce a straightforward method to measure organizational diversity that utilizes the importance of social ties. In developing our single-mode brokerage-based measure of diversity, two themes are central. The first stems from the work that has emphasized the benefits of increased diversity for organizational decision-making, while the second draws on the insights from social network analyses to consider the roles that individuals play in connecting individuals not already affiliated. The paper is organized into three sections: the first contextualizes our measure within the work that has theorized the importance of brokerage, homophily, and organizational diversity; the second describes and outlines the steps for deconstructing brokerage roles that is necessary to generate our measure of organizational diversity; and the third contributes to the literature by applying our brokerage-based measures of diversity to an exploration of whether increased levels of demographic diversity is related to greater corporate responsibility in 2005 as indicated by lower numbers of regulatory violations among the Fortune 100.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".