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Record W2038475055 · doi:10.1068/d304

Lives Lived and Lives Told: Biographies of Geography's Quantitative Revolution

2001· article· en· W2038475055 on OpenAlexaff
Trevor J. Barnes

Bibliographic record

VenueEnvironment and Planning D Society and Space · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Geography and Geographical Thought
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiographyHierarchyArgument (complex analysis)SociologyRationalityEpistemologySocial scienceHistoryArt historyPhilosophyLawPolitical science

Abstract

fetched live from OpenAlex

In this paper I draw upon both biographical and sociological approaches to examine one moment in the history of geography's quantitative revolution of the late 1950s and early 1960s: the publication of Brian Berry and William Garrison's paper, “The functional bases of the central place hierarchy”, in Economic Geography in 1958. The origins of that paper are traced through the life stories—the ‘lives told‘—of the two authors. In particular, I try to connect the specific life trajectories of Berry and Garrison up until 1958 with the wider social and cultural contexts in which they lived. The theoretical impetus for the study are three literatures: the first is science studies, and especially the work of Bruno Latour and his ideas of ‘black boxing’ and ‘translation’; the second is on the history and sociology of quantification; and the third is on biography, particularly scientific biography. The broader argument of the paper is that the seemingly disembodied numbers, calculations, and precisely drawn figures and graphs that increasingly inflect human geography from the late 1950s, and found in such papers as Berry and Garrison's, are socially embedded, a consequence not of a universal rationality but of specific lives and times that infuse the very substance of the works produced.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.008
Science and technology studies0.0100.050
Scholarly communication0.0120.019
Open science0.0010.006
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.021
GPT teacher head0.259
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations122
Published2001
Admission routes1
Has abstractyes

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