Xenophobia Denialism and the Global Compact for Migration in South Africa
Bibliographic record
Abstract
The United Nations (UN) Global Compact for Safe, Orderly and Regular Migration (GCM) commits signatories to eliminate all forms of discrimination, and to condemn and counter expressions, acts and manifestations of racism, racial discrimination, violence, xenophobia and related intolerance. The growth of xenophobia across the global South has become increasingly apparent. Governance responses to anti-immigrant sentiment and action take three main forms: intensification, mitigation and displacement. In South Africa, policy on international migration to the country focuses more on the perceived negative impacts of migration than any potential development benefits. As a direct result, negativity pervades both public policy and popular discourse about migrants and their impact on the country. Migrants encounter an extremely hostile environment in which their constitutional and legal rights are abrogated, their ability to access basic services and resources is constrained, and their very presence in the country is excoriated by the state and citizenry. Xenophobic attitudes are deeply entrenched, and xenophobic attacks have become common. In this context, this chapter examines the response of the national government and argues that displacement is the dominant governance model. This takes two forms: xenophobia denialism and the scapegoating of migrants. Xenophobia denialism and blaming migrants for their own victimisation act as barriers to South Africa recognising, promoting and arguing for migration as a positive developmental tool and operationalising the anti-xenophobia provisions in the Global Compact.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".