Effects of Movements and Opportunities on the Adoption of Same-Sex Partner Health Benefits by Corporations
Bibliographic record
Abstract
In this study, we draw upon a social movement perspective to examine how movements and institutional opportunity (political and cultural) influenced a sample of Fortune 500 corporations’ adoption of a controversial organizational practice—same-sex partner health benefits. Our results show that while corporations’ gay, lesbian, bisexual, and transgender (GLBT) employee resource groups increased the rate of the corporations’ benefits adoption, the effect of the GLBT employee resource groups became weaker when the degree of resource concentration of local GLBT advocacy organizations was high. Political opportunity derived from state legal environments and cultural opportunity derived from the tenor of moral legitimacy in leading national press coverage had little influence on the rate of benefits adoption. Furthermore, the influence of a GLBT employee resource group on the rate of benefits adoption by its corporation became weaker when cultural opportunity, derived from increases in positive tenor of pragmatic legitimacy discourse used by movement and countermovement organizations in the press, was present. Accordingly, our study shows the complicated effects of movements within and outside corporations and cultural opportunity on the adoption of a controversial practice and reveals the importance of mobilizing structure (both internal and external movements) and cultural opportunity in the adoption.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".