Golden Wildebeest Days: Fragmentation and Value in South Africa’s Wildlife Economy After Apartheid
Bibliographic record
Abstract
There are renewed global efforts to make wildlife conservation the foundation for broad-based economic development. This article looks at these tendencies in the ‘Kruger to Canyons’ (K2C) biosphere region in South Africa, encompassing the Kruger National Park and adjacent settlement areas and reserves. Various forms of the wildlife economy have a long history in this region. However, it is increasingly posited as a preternatural means for creating jobs. We chronicle the growth of the wildlife economy from its apartheid heyday to the present, showing its fundamental dependence on the ecological and political fragmentation of space. More generally, these biopolitical divisions are part of a broad contestation of wildlife value, organised around changing regimes of protected area enclosure and the spacing of human and non-human life. Despite recent claims by the South African conservation industry that it is demolishing fences and increasing habitat connectivity, political territorialisation and ecological fragmentation continue to be important means of securing profit and reducing perceived risk. While the contradictions of this dynamic have now become acute through the emergence of the rhino-poaching crisis, the growth of that violent industry, we conclude, should not be seen as the negative inversion of a legal wildlife economy. Instead, both the legal and the illegal wildlife economies are manifestations of the same underlying problems: ill-conceived attempts at agrarian reform; the persistent influence of an older veterinary wildlife assemblage; the continued role of the rural poor as an enabling but unacknowledged buffer between development and wildlife.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".