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Record W2075854272 · doi:10.1080/00420980600776459

Cities and Workplace Communication: Some Quantitative French Evidence

2006· article· en· W2075854272 on OpenAlexaff
Sylvie Charlot, Gilles Duranton

Bibliographic record

VenueUrban Studies · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDemiseFace (sociological concept)Information and Communications TechnologyFocus (optics)BusinessContrast (vision)Face-to-face interactionEmpirical evidencePublic relationsDemographic economicsMarketingEconomic geographyEconomicsSociologyPolitical scienceComputer scienceSocial science

Abstract

fetched live from OpenAlex

This paper uses a unique labour force survey to document workplace communication patterns in urban, suburban and rural areas. A number of interesting stylised facts are distilled relating to workplace communication: its intensity, within-firm communication, communication external to the firm and the media being used with a special focus on information technologies. The paper also investigates the complementarities across different media of communication and the evolution over time of workplace communication patterns. It is found that cities foster communication external to the firm and the use of telecommunications. By contrast, the hypothesis of a greater prevalence of face-to-face communication in cities does not receive much empirical validation. Also, complementarities across media do not lend much support to popular predictions about the forthcoming demise of cities following the replacement of face-to-face meetings by telecommunications.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.146
Threshold uncertainty score0.291

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.011
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.097
GPT teacher head0.270
Teacher spread0.173 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations83
Published2006
Admission routes1
Has abstractyes

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