Bibliographic record
Abstract
The place for the teaching of and research dealing with the history of economics continues to draw attention, at least from within our speciality. Recent examples are the History of Political Economy mini-symposium (1992) that was anchored to Margaret Schabas's paper and Donald Walker's affirming essay in theJournal of the History of Economic Thought(1999). The former asked whether the history of economics, given the neglect shown by mainstream economics, should be incorporated into history of science programs. The latter argued that this neglect is unfortunate because there is a place for the history of economics in the training of economists. (The neglect is documented by Aslanbegui and Naples (1997).) An important feature of Walker's essay is that the argument was based on structural aspects of the discipline. His point of departure was Donald Gordon's (1965) claim that there is little in the history of economic analysis that facilitates the understanding of current economics and the research problems with which it is concerned. Certainly the education of natural scientists, as Gordon noted, is consistent with this claim. George Stigler (1969) made a similar claim, and both more or less took the conclusion to be self-evident. Walker's response was that, at least among a variety of important sub-disciplines, there is not professional consensus and, therefore, there may not be a sharp distinction between current and past theory. This suggests that some form of historical analysis may be useful. Walker's paper moved the discussion away from arguments that begin with the presumption that every educated economist should be versed in the origins of economics. Instead, he focused on questions relating to history as a tool for understanding and for doing today's economics. In this respect, Walker returned to an issue addressed by Filippo Cesarano (1983)—they both focused on historical studies as a source of illumination for current economic analysis.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".