Bibliographic record
Abstract
After the passage of a century and the appearance of countless publications on the South African War, there remain the most profound differences of opinion about the war's origins. Many of the more recent accounts have emphasised Britain's economic interests in the southern African periphery – principally in the production and supply of gold – and the consequent necessity of removing the administratively backward and economically obstructionist regime of Paul Kruger in Pretoria. Other accounts have stressed the concerns of British government decision-makers at the imperial centre – concerns about British power and prestige, about the necessity of maintaining British paramountcy in southern Africa, and about safeguarding the strategically vital Cape route. Further divisions exist between those accounts which stress the broader structural forces at play and those which give a central role to key individuals such as Sir Alfred Milner (Britain's high commissioner and regional proconsul for southern Africa) or Joseph Chamberlain (the colonial secretary in London). This chapter will attempt to explain the War's origins through an analysis combining the broader economically driven developments in southern Africa; the geopolitical concerns of decision-makers in London; and the influence of Milner as the key ‘man on the spot’ and intermediary between the periphery and the centre. This explanation will be based on a model of imperial expansion first proposed by Ronald Hyam.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".