Taking Sides: The Interactive Influences of Institutional Mechanisms on the Adoption of Same-Sex Partner Health Benefits by Fortune 500 Corporations, 1990–2003
Bibliographic record
Abstract
We draw upon institutional theory to investigate the interactive influences of institutional mechanisms—coercive, mimetic, and normative—on the diffusion of a controversial and socially stigmatized practice, same-sex partner health benefits, in Fortune 500 corporations between 1990 and 2003. Given the social stigma associated with domestic partnerships of lesbians and gay men during the period of the study, the provision of these benefits was highly controversial and induced intense contestation between proponents and opponents of the institution of equal treatment for lesbian and gay employees. We explore the diffusion of theses benefits using data on cumulative adoptions by similar others, state laws forbidding discrimination based on sexual orientation, and overall tenor in press coverage of the benefits. Our analysis shows that the cumulative number of adoptions within industry increased the positive effect of state laws on the corporation's decision to provide the benefits. However, the cumulative number of adoptions in the state of the corporation's headquarters decreased the positive effects of both state laws and overall tenor in press coverage on such a decision. Accordingly, our study contributes to institutional theory by pointing to complex interactive influences of institutional mechanisms on the institutionalization of contested practices, and to the literature on lesbian and gay issues in the workplace by studying factors influencing organizational decisions to adopt policies supportive of lesbian and gay employees.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".