Bibliographic record
Abstract
This issue of The Review revisits issues of governance, civil society and human rights in Africa. It does so just as the issue reappears on the agenda of international agencies and western governments. Indeed, this year's fashionable new ideological robes (as far as development and the South are concerned) is the idea that economic growth will be driven by greater political participation. This lies at the heart of the propositions being advanced by various donors about a 'multifaceted development challenge' aimed at creating a 'window of opportunity' to end 'the marginalisation of Africa's people'. Like all the other formulations articulated from this quarter over the last thirty years, it deserves analysis less for what it might do for Africans than for what it is likely to do to them; in other words, for what it tells us about its imperialist design.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".