Escaping the multicultural trap: strategic litigation and the decriminalization of same-sex relations in Mauritius
Bibliographic record
Abstract
Mauritius is widely praised for policies that have minimized tensions between ethno-religious groups. Other types of groups, however, are excluded from that sociopolitical ordering of society. In particular, LGBTQI+ Mauritians have long been stigmatized and criminalized by an anti-sodomy law, enacted by the British colonial administration. Despite a reputation for a strong commitment to human rights, the Mauritian government never repealed that law. Drawing in large part on interviews with LGBTQI+ activists in Mauritius, this article analyzes how and why they challenged that anti-sodomy law in court, what they achieved and what the implications are. It argues that the state’s and political parties’ emphasis on traditional cultures made it difficult to convince the government to act, leaving strategic litigation as the best option for getting LGBTQI+ rights recognized. Grounded primarily in constitutionally guaranteed rights and harnessing support from international nonstate actors, their court cases succeeded in carving out greater space for LGBTQI+ rights within the conservative multicultural framework, even though the verdict was rather limited in scope. Significant pressure will be needed to achieve more political and social change, potentially including more strategic litigation, which poses a significant challenge to LGBTQI+ activists.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.025 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".