‘In the beginning was economic geography’ – a science studies approach to disciplinary history
Bibliographic record
Abstract
Science studies are an increasingly prominent interdisciplinary body of work. Now a diverse literature, one of its most consistent and common themes is a reluctance to accept the standard model of scientific explanation (‘internalism’) that conceives scientific knowledge, and the disciplines with which it is associated, as the product of a rationality that is progressively realized over time. Instead, science studies emphasize the importance of local circumstances in shaping knowledge, which, in turn, makes such knowledge messy and context-dependent. The purposes of this paper are twofold. The first is to provide a selective review of science studies. In particular, the paper recognizes three subtraditions within the larger genre: Mertonian institutionalism, the sociology of scientific knowledge, and cultural studies of science. The second purpose is to begin developing a case study in order to apply such literature, that of the institutional origins of economic geography during the late nineteenth and early twentieth centuries, and linked to a series of wider social processes around commercial trade and imperialism. To make the case study manageable, I concentrate on only two authors and their respective key books: the Scottish geographer George Chisholm, who wrote the first English-language economic geography textbook, A handbook of commercial geography (1889); and the American geographer J. Russell Smith, author of the first US college text in economic geography, Industrial and commercial geography (1913).
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.005 | 0.047 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".