A comment on Dincecco et al. (2022): Pre‐colonial warfare and long‐run development in India
Bibliographic record
Abstract
Abstract We test the reproducibility and replicability of M. Dincecco, J. Fenske, A. Menon and S. Mukherjee (2022), which reports a positive relationship between pre‐colonial interstate warfare and long‐run development patterns across India. Overall, we confirm that all of the study's estimates are computationally reproducible using the provided replication package in Stata, but note that the ease of replication could be improved by the provision of code and intermediate data sets for the conflict exposure measure. We test for and find no evidence of data manipulation in the final data sets. Concerning direct replicability, we consider different ways of measuring distance to conflicts and also alternative proxies for both the dependent variable and variables that capture channels by which the main effects operate. We find that some estimates are sensitive to the type of conflict considered. Other estimates are sensitive to the time period considered, most likely due to time heterogeneity in the number of conflicts recorded. Nevertheless, most estimates are substantially in line with the original study.
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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 | 0.025 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.034 | 0.037 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".