Ruth First Prize: James Musonda on the 2021 donchi-kubeba (don’t tell) elections in Zambia
Bibliographic record
Abstract
This is a provocative, thought-provoking and radical analysis of the 2021 election in Zambia when the ruling party was unexpectedly overthrown.With its foundation in extensive firsthand research, participant observation and activist immersion, Musonda's account is a worthy example of Ruth First's methodology in Mozambique in the 1970s as well as of her commitment to class analysis relevant to a particular time and place.Compared to the usual explanatory framing of African elections in uncritical terms of clientelism and ethnic arithmetic, Musonda here undertakes a study showing how the ' politics of the belly' (Bayart 1993) can be subverted.His account takes on the way the Zambian political class held on to power through its ruling party via bribery, violence and oppression and through its linkage with the copper mining companies which dominate Zambia's economy.Copper, once nationalised, is now privatised and trade union activity has declined.Elections offer an opportunity for sheer numbers to confront state and capitalist power -an opportunity usually forcibly suppressed.However, the gross failures of the Zambian government and its economic impoverishment of the population have sparked bitterness and a growing awareness that there might be ways to resist.There is a nuanced account of class relations here -never simply 'elite/mass' oversimplifications, but distinctions made and evidenced between the diversity of organised workers and auxiliary
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".