The immigration Issues in the Post-Apartheid South Africa: Discourses, Policies and Social Repercussions
Bibliographic record
Abstract
The paper provides a critical appraisal of changes that have marked the political discourses on immigration from within and outside the Southern Africa region in the aftermath of the 1994 elections in the Republic of South Africa. It also provides insights into the connections of these changing discourses with the rise of social intolerance of immigration from other African countries. The immigration policy in the apartheid period was highly controlled and somewhat in favour of immigration of foreign labour within the Southern Africa region. In the post-apartheid period, the imperative of social reconstruction and with it that of universalism in service delivery have induced a shift in the discourse on immigration, making it markedly exclusionary and selective, since the country has had concurrently to deal with increased immigration flows (refugees, irregular and regular) and shortage of skills. This has had some social repercussions reflected in the negative public perception of immigration, especially that of African origin. The prevailing climate of xenophobia and other forms of social harassment towards African foreigners, accompanying the immigration discourses, finds its roots in the social representations brought, locally, about by the new dispensation of the post-apartheid regime and, internationally, by the redefinition of the strategic positioning of South Africa in the context of globalization.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.030 | 0.033 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| 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".