Bibliographic record
Abstract
Some one hundred years ago, South Africa was torn apart by the Second AngloBoer WBI (1899-1902). The WaI was a colossal psychological experience fought at great expense. It cost Britain twenty-two thousand men and £223 million. The social, economic and political cost to South Africa was greater than the statistics immediately indicate: at least ten thousand fighting men in addition to the camp deaths, where a combination of indifference and incompetence resulted in the deaths of 27 927 Boers and atleast 14 154 black South Africans. Yet these numbers belie the consequences. It was easy for the British to "forget" the pain of the War, which seemed so insignificant after the losses sustained in 1914-18. With a long history of far-off battles and foreign wars, the British casualties of the Anglo-Boer War became increasingly insignificant as opposed to the lesser numbers held in the collective Afrikaner mind. This impact may be stated somewhat more candidly in terms of the war participation ratio for the belligerent populations. After all, not all South Africans fought in uniform. For the Australian colonies these varied between 4,5 per thousand (New South Wales) to 42,3 per thousand (Tasmania). For New Zealand there were 8 per thousand, for Britain 8,5 per thousand, and for Canada 12,3 per thousand; while in parts of South Africa this was perhaps as high as 900 per thousand. 2 The deaths and high South African paiticipation ratio, together with the unjustness of the war in the eyes of most Afrikaners, introduced bitterness, ifnot hatred, which cast long shadows upon twentieth-century South Africa.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".