Where Else Would you Look? Constructivism and the Historiography of Economics
Bibliographic record
Abstract
Eleven years ago, at my first HES conference, I attended a session on the historiography of economics. In my naiveté and brashness (not a good combination!) as a grad student, I asked why so much of the conversation about how to do historical work in economics was dominated by reference to methodological categories provided by philosophers of science like Lakatos, Popper, and Kuhn. I enjoyed my own investigations in the philosophy of economics, and was starting to write about Frank Knight, who was certainly not naive philosophically, but I wanted a different reference point as a historian of economics. I remember one member of the panel looking at me somewhat puzzled and asking, “Where else would you look?” Today, we gather to reflect, once again, on the historiography of economics. This time, we are asked to reflect on a new reference point for our work: the sociology of scientific knowledge or science studies. Jan Golinski has provided us with an excellent survey of work in this field, and a synopsis of its relevance over the past fifteen years or so to the history of the natural sciences. His book is not a rallying cry for constructivism, but rather a careful analysis of the benefits and costs to historians of science if they choose to adopt constructivist techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.052 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".