Controversy, Facts and Assumptions: Lessons from Estimating Long Term Growth in Nigeria, 1900–2007 (SWP 13)
Bibliographic record
Abstract
This article contributes to the debates surrounding ‘New African Economic History’ by exploring the feasibility of constructing a time series of economic growth in Nigeria spanning the 20th century. Currently most datasets for African economies only go back to 1960. The sources for their creation exist, but these valuable colonial data remain underutilized. This is a first exploratory paper in a project aiming to create measures of economic growth through the 20th century for a sample of African economies. The paper offers a systematic discussion of the different available datasets on population, agricultural production and income for the country. It finds that the existing data, often presented as facts, are more accurately described as projections based on assumptions. If these assumptions are already made in the production of the data, this precludes empirical testing of important questions. The main lesson is that any African economic history investigation must both begin and end with a critical analysis of the quantitative data, and must further be supported by careful qualitative evaluation.
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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 | 0.015 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".