The hares, the hounds and the African National Congress: on joining the Third World in post-apartheid South Africa
Bibliographic record
Abstract
The utility of framing questions of global inequality in relation to a ‘First World’ and a ‘Third World’, a North and a South, or developed countries and developing (or underdeveloped) countries, has been much debated since the end of the Cold War. This article addresses the issue of the perceived weaknesses and possible continued strengths of the notion of the ‘Third World’ in general terms, and then grounds such a discussion through an analysis of the way that the African National Congress (anc) government in post-apartheid South Africa has approached the question of global inequality. Since its election in 1994, and more particularly since Thabo Mbeki succeeded Nelson Mandela as president, the anc has presented itself as having an especially important leadership role on behalf of the Third World. The profound contradictions inherent in the anc's effort both to retain its Third Worldist credentials and to present itself as a reliable client to the Bretton Woods institutions and foreign investors provides insights into how to design alternative strategies for overcoming world-wide poverty, strategies which might be more effective than those chosen by the anc. Since the anc was elected to government in 1994 it has pursued a brand of deeply compromised quasi-reformism, analysed here, that serves primarily to deflect consideration away from the options presented by other, much more meaningfully radical international and South African labour organisations, environmental groups and social movements. At the present juncture a range of increasingly well-organised grassroots movements in South Africa find that they have no choice but to mobilise in active resistance to the bankrupt policies of the anc. The increasing significance of these efforts points to the possibility that they might eventually be able to push South Africa—either through a transformation of the anc itself or through the creation of some new, potentially hegemonic, political project in that country—back into the ranks of those governments and groups that seek to use innovative and appropriately revolutionary approaches to challenge the geographical, racial and class-based hierarchies of global inequality.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.017 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".