The discipline that came in from the cold: American human geography becomes a Cold War social science
Bibliographic record
Abstract
The high–Cold War period – from the Truman doctrine to the Cuban missile crisis – brought not only profound changes in geography but also profound changes to Geography. The discipline moved from a museum-like, fusty subject to aspiring to be a Cold War, cutting-edge hybrid social science (or behavioural science) ‘cross-bred’ with physical and applied sciences. The vehicle was social physics. Its origins were in the 17th century, but the actual name was not coined until 1835 and then in French, ‘Physique Sociale’, by the Belgian astronomer, statistician and social tabulator, Adolphe Quetelet (1796–1874). As a project, social physics rested on the belief of monism in which the same formal explanatory scientific principles held in both natural and social worlds. In the particular case of Cold War geography, monism meant asserting that specifically Newtonian equations describing the movements of celestial bodies in the heavens could explain equally as well movements of human bodies down on earth. In American human geography, social physics was pioneered from the early 1950s by William Warntz, who collaborated early on with the Princeton astrophysicist, John Stewart, who, in turn, had previously worked with the Harvard linguist, George Zipf. Together Warntz and Stewart using home-made early computing devices and drawing on Newtonian formulations of potential cast geography as social physics, extolling its virtues as ‘macrogeography’ and on par with other disciplines that already had entered the Pantheon of Cold War social science.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.040 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.009 |
| 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".