Writing history backwards or sideways: towards a consensus on <scp>A</scp>frican population, 1850–2010
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 aims to make an empirical and theoretical contribution towards the creation of a continent‐wide dataset on A frican population extending into the pre‐1950 era. We investigate the reliability and the validity of the current population databases with the aim of working towards a consensus on the long‐term series of A frican total population with a reliable 1950 benchmark. The cases of K enya, N igeria, and G hana are explored to show the uneven coverage of census taking in colonial and post‐colonial A frica and to demonstrate the need for an upward adjustment of the conventional 1950 benchmark. In addition, we discuss the advantages and disadvantages of M anning's approach of projecting population growth estimates backwards in time by adopting the available Indian census data as A frican ‘default growth rates’, and we propose an alternative approach by incorporating the demographic experiences of tropical land‐abundant countries in S outh‐ E ast A sia.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.007 |
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