Covert allyship: Implementing <scp>LGBT</scp> policies in an adversarial context
Bibliographic record
Abstract
Abstract This study introduces the concept of covert allyship as a strategy for tacitly supporting lesbian, gay, bisexual, and transgender (LGBT) inclusion in adversarial contexts. Drawing on a qualitative case study of 12 Western multinational enterprises (MNEs) operating in Indonesia, the largest Muslim country in the world, the article sheds light on how allyship for LGBT issues is undertaken covertly as allies seek to transcend tensions arising between headquarters publicly advocating for LGBT rights and their subsidiaries. The findings evaluate both barriers to MNE subsidiaries implementing LGBT‐supportive policies and facilitating mechanisms for covert forms of institutional allyship. Finally, the article provides recommendations for how MNEs can adopt practices that build subtle yet effective LGBT‐supportive approaches in contexts that require sensitivity to local cultures and legislation.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".