Development Economics and Method: A Quarter Century of ABCDE
Bibliographic record
Abstract
This, the twenty-fifth anniversary of the Annual Bank Conference on Development Economics (ABCDE) and also of the Washington Consensus, is a good time to take stock of development economics. What have we learned? What do we need to unlearn? What is the right methodology for development economics so that future knowledge is on firmer footing? These are important questions and this year’s ABCDE and this introduction is a stocktaking of where we stand on these questions. Development economics has come a long way from Adam Smith’s towering achievement in putting the discipline on a firm theoretical footing to some great strides in empirical methods in recent times. What we need to focus on now is the blending of analytics, statistics and intuition; and of using theory to integrate disparate empirical research.
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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 | 0.064 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.023 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".