Bibliographic record
Abstract
Abstract Method myopia is defined as theoretical rhetoric absent empirical discernment regarding flavor‐of‐the‐day econometric methodologies. This Fellows Address discusses why method myopia is pervasive, what factors contribute to the pervasiveness, why is likely it to increase, and finally, a possible remedy. To that end, incentive structures facing researchers, reviewers, and editors are considered within the life‐cycle of a typical econometric methodology. Considering our discipline is empirically driven, there are potentially large costs by using flavor‐of‐the‐day methodologies when an alternative ‐‐ possibly leading to different economic results and policy responses ‐‐ is the appropriate method. Furthermore, method myopia can notably restrict the set of research problems examined thereby creating additional, potentially large, opportunity costs. Finally, over‐selling the superiority/completeness/correctness of results from such flavor‐of‐the‐day methodologies to policy makers can not only be costly in the particular case, but can undermine the long‐term credibility of our disciplinary advice to policy makers.
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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 | 0.307 | 0.558 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.031 | 0.011 |
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".