If Nigeria’s Economic Performance From 1960–2020 Resembled China’s and South Korea’s: Exploring the Counterfactual and Subjunctive in Economics
Bibliographic record
Abstract
This paper extends earlier work on Nigeria’s failure to improve living standards for its population when compared with China and South Korea since 1960. The paper employs the use of counterfactuals (‘‘alternate histories and contingent futures”) -- what might have happened had a particular historical event not occurred or occurred differently -- that has long been the domain of historians, not economists. First, I present the main reasons for Nigeria’s poor performance over the last 60 years. Then I consider the “fantasy world of the counterfactual”: What living standards would Nigeria have today if Chinese or South Korean demographic and economic growth rates were applied over this 60 year interval? The value of this “invented scenario” lies in “assigning a cost” to decades of failures in governance and economic management.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".