Users and producers of African income: Measuring the progress of African economies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it