Symposium on <i>Marshall's Tendencies</i>: 4 Comments on <i>Marshall's Tendencies</i>
Bibliographic record
Abstract
Professor Sutton opens his lively monograph on the nature of economic theory with the following question: is it possible to find economic models that work? He uses the question to guide us on a methodological tour with Marshall's characterization of economic theory as the point of departure. I must say I enjoyed the trip. Along the way, the animating issue of what works in economics could hardly have been addressed without dealing with issues in verification, and the author's arguments include an appraisal of what he considers as standard econometric methods. In these comments, I will revisit at some length one of the key sites on John Sutton's tour bringing along a view of modern econometrics which is somewhat different from his and which affords a different perspective on the Marshallian paradigm.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.020 | 0.034 |
| Insufficient payload (model declined to judge) | 0.015 | 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".