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Record W3123491446

How Did Location Affect Adoption of the Commercial Internet? Global Village vs. Urban Leadership

2006· article· en· W3123491446 on OpenAlexaff
Chris Forman, Avi Goldfarb, Shane M. Greenstein

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThe InternetFrontierAffect (linguistics)BusinessRural areaMarketingAdvertisingGeographyPolitical scienceSociology
DOInot available

Abstract

fetched live from OpenAlex

We provide a framework and evidence to confront two contradictory yet common assertions: (1) new technology such as the Internet favors businesses in urban areas and (2) the Internet reduces the importance of distance for economic activity. Controlling for other factors, we show that participation in the Internet is more likely in rural areas than in urban areas. Use of frontier technologies also arises in rural areas for interfirm technologies, which are associated with ending economic isolation. Nevertheless, talk of the dissolution of cities is premature. Frontier Internet technologies for intrafirm communication and advanced computing appear more often at establishments in urban areas, even with industry controls. Major urban areas also contain many establishments from information technology-intensive industries, whose presence could reinforce the concentration of frontier Internet technologies in these areas. (JEL

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.021
GPT teacher head0.221
Teacher spread0.200 · 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 teacher head, 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

Citations7
Published2006
Admission routes1
Has abstractyes

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