Bibliographic record
Abstract
The politics of Zimbabwe have been closely linked – much for the worse – to the fortunes of one man, Robert Gabriel Mugabe. Born in 1924, the wily octogenarian has led his state from prosperity to utter misery, with a ruined economy, a collapsed public health system, and a hungry and sometimes starving population. A massive ten-month cholera outbreak beginning in August 2008 is only the latest in a series of disasters. Details of how such a massive catastrophe occurred require far more space than is available here, but a short overview shows how this failed state has ruined the lives not only of its own population, but of thousands more living in the Southern African region. Mugabe rose to prominence in the 1960s as the secretary-general of the Zimbabwe African National Union (ZANU). Frequently threatened and sometimes jailed by the dominant white minority in what was then Southern Rhodesia, Mugabe fled the country in 1974 to join the “Second Chimurenga,” as the war for liberation was called. Mugabe emerged victorious at the end of the war in 1979 and won a sweeping victory in the general elections the next year. With the help of violent intimidation, Mugabe as prime minister consolidated power over ZANU's rival, the Zimbabwe African Peoples' Union (ZAPU), led by Joshua Nkomo. ZAPU drew most of its support from the Ndebele-speaking region in the south, centered on Bulawayo, the country's second city, whereas ZANU was dominated by Shona-speaking peoples in Harare and in rural Zimbabwe outside Matableland.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".