Users and producers of African income: Measuring the progress of African economies
Bibliographic record
Abstract
This article traces how African incomes have been measured through history, and shows that there has been a conflict of aims between producers and users of national income estimates. Politicians and international organizations seek income measures that reflect current political and economic priorities and achievements. Thus the importance given to markets, the state, and peasants in the estimates varies through time and space. Meanwhile statisticians aim to produce a measure that gives the best possible reflection of the economy given the available data and definitions at any time. Scholars prefer a measure that is consistent through time and space so that ‘progress’ can be measured, compared, and analysed, while not being able to reach consensus on how ‘progress’ is best calculated or defined. The result is not an objective measure of progress, but rather an expression of development priorities determined by changes in the political economy. The article provides a much-needed study of the ability of the statistical offices to provide income statistics independently and regularly. These data are of crucial importance as they enter the public domain in policy evaluations, political debates, and progress towards lofty aims such as the Millennium Development Goals.
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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 | 0.020 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".