Replication and Reproducibility in Social Sciences and Statistics: Context, Concerns, and Concrete Measures
Bibliographic record
Abstract
Replicability is at the core of the scientific enterprise. In the past 30 years, recurring concerns about the extent of replicability (or lack thereof) of the research in various disciplines have surfaced, including in economics. In this talk, I describe the context in which the current discussion in the social science is occurring: what are the definitions of replicability and reproducibility, what is failing, and to what extent. In particular, I discuss the concerns in economics: to what extent is this a problem in economics, what are the approaches that are being considered, and what are the possible broader implications of those approaches. Finally, I discuss the concrete measures that are being implemented under my guidance at the American Economic Association, and that are being discussed in the broader economics community. The solutions to these problems will change the way research will be taught and conducted, in economics in particular, and in the social sciences more broadly. The implications affect undergraduate and graduate teaching, research infrastructure, and habits. Presented at Montreal Applied Micro Workshop (Montreal, Canada) on 2019-02-21
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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 | 0.764 | 0.887 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.008 | 0.101 |
| Scholarly communication | 0.026 | 0.038 |
| Open science | 0.011 | 0.019 |
| Research integrity | 0.020 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".