Performing Economic Geography: Two Men, Two Books, and a Cast of Thousands
Bibliographic record
Abstract
In this paper I use the notion of performance, especially as it has been theorized within the science studies literature, to begin to make sense of the history and continuing practices of economic geography. I argue that not only humans perform, but also objects. In this paper, I focus on the performance of books, and in particular, textbooks, or as Bruno Latour calls them, ‘immutable mobiles’. I argue that textbooks bring four attributes to their performance: they travel easily over distance, thereby bringing their message to a geographically diffuse audience; they allow for ‘an optical and semiotic homogeneity’, that is, they take quite different pieces of the world, and bring them together, manipulating them and controlling them, on the same page; they represent an obligatory passage point in the sense that once they are accepted as the standard summary of a field they are necessarily acknowledged by successors; and finally, their effectiveness is in part a consequence of their rhetoric—defined as the ability to draw together and integrate within the text a wide range of sources and authors. These arguments about the performance of textbooks are illustrated by two case studies. The first is George G Chisholm's Handbook of Commercial Geography, published in 1889, which helps launch economic geography as an academic discipline within Anglo-America. The second is Peter Haggett's Locational Analysis in Human Geography, published in 1965, which in many ways codifies the quantitative and theoretical revolution that first emerged in the United States in the late 1950s.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".