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Record W2153441878 · doi:10.1177/0042098013506047

Cities and the geographical deconcentration of scientific activity: A multilevel analysis of publications (1987–2007)

2013· article· en· W2153441878 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueUrban Studies · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSpatial and Panel Data Analysis
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
FundersAgence Nationale de la Recherche
KeywordsUrban agglomerationRegional scienceEconomic geographyContradictionCompetition (biology)GeographyPolitical science

Abstract

fetched live from OpenAlex

Most current scientific policies incorporate debates on cities and the geographic organisation of scientific activity. Research on ‘world cities’ develops the idea that interconnected agglomerations can better take advantage of international competition. Thus, the increasing concentration of activities in these cities at the expense of others could be observed by certain scholars using measures based on scientific publications. Others, however, show that an opposite trend is emerging: the largest cities are undergoing a relative decline in a country’s scientific activities. To go beyond this seeming contradiction, this paper provides a global analysis of all countries with papers in the Web of Science over the period 1987–2007. The author’s addresses were geocoded and grouped into agglomerations. Registering of papers was based on the fractional counting of multi-authored publications, and the results are unambiguous: deconcentration is the dominant trend both globally and within countries, with some exceptions for which explanations are suggested.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.071
GPT teacher head0.256
Teacher spread0.185 · 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