Regional Migration Governance in the Southern African Development Community: Gender-Neutral, Gender-Blind or Gender-Biased?
Bibliographic record
Abstract
Growing attention is being paid to the role and potential of regional institutions in the governance of migration and protection of migrant rights. Southern Africa presents an illustrative example of the multiple challenges to regional migration governance, being a region of uneven governance capacity and rights regimes and weak regional institutions. Although there has been some move towards regional harmonization of migration laws, policies and management in the Southern African Development Community (SADC), formal migration governance remains entrenched in national laws and structures or, at best, bilateral agreements, specifically on migrant labor. Meanwhile, migrants have difficulty securing even those basic social services and human rights protections to which they are legally entitled. This is especially the case for female migrants, most of whom work in the informal sector outside formal mechanisms of regulation and protection. This paper examines regional migration governance in SADC from a gender perspective, including the implications of a developmentalist rather than rights-based framing of regional migration policy discourse. It draws on empirical findings from research on gender and cross-border migration to assess whether superficially gender-neutral regional policy and discourse on migration is likely to have unequal gender outcomes in terms of migrants’ rights and livelihoods.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".