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Record W2963910803 · doi:10.2478/jdis-2018-0014

Factors Influencing Cities’ Publishing Efficiency

2018· article· en· W2963910803 on OpenAlexaboutno aff
György Csomós

Bibliographic record

VenueJournal of Data and Information Science · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSpatial and Panel Data Analysis
Canadian institutionsnot available
FundersEuropean Social FundEuropean Commission
KeywordsPublishingBeijingRegional scienceQuality (philosophy)Library sciencePolitical scienceChinaGeographyComputer scienceLaw

Abstract

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Abstract Purpose Recently, a vast number of scientific publications have been produced in cities in emerging countries. It has long been observed that the publication output of Beijing has exceeded that of any other city in the world, including such leading centres of science as Boston, New York, London, Paris, and Tokyo. Researchers have suggested that, instead of focusing on cities’ total publication output, the quality of the output in terms of the number of highly cited papers should be examined. However, in the period from 2014 to 2016, Beijing produced as many highly cited papers as Boston, London, or New York. In this paper, another method is proposed to measure cities’ publishing performance by focusing on cities’ publishing efficiency (i.e., the ratio of highly cited articles to all articles produced in that city). Design/methodology/approach First, 554 cities are ranked based on their publishing efficiency, then some general factors influencing cities’ publishing efficiency are revealed. The general factors examined in this paper are as follows: the linguistic environment of cities, cities’ economic development level, the location of excellent organisations, cities’ international collaboration patterns, and their scientific field profile. Furthermore, the paper examines the fundamental differences between the general factors influencing the publishing efficiency of the top 100 most efficient cities and the bottom 100 least efficient cities. Findings Based on the research results, the conclusion can be drawn that a city’s publishing efficiency will be high if meets the following general conditions: it is in a country in the Anglosphere–Core; it is in a high-income country; it is home to top-ranked universities and/or world-renowned research institutions; researchers affiliated with that city most intensely collaborate with researchers affiliated with cities in the United States, Germany, England, France, Canada, Australia, and Italy; and the most productive scientific disciplines of highly cited articles are published in high-impact multidisciplinary journals, disciplines in health sciences (especially general internal medicine and oncology), and disciplines in natural sciences (especially physics, astronomy, and astrophysics). Research limitations It is always problematic to demarcate the boundaries of cities (e.g., New York City vs. Greater New York), and regarding this issue there is no consensus among researchers. The Web of Science presents the name of cities in the addresses reported by the authors of publications. In this paper cities correspond to the spatial units between the country/state level and the institution level as indicated in the Web of Science. Furthermore, it is necessary to highlight that the Web of Science is biased towards English-language journals and journals published in the field of biomedicine. These facts may influence the outcome of the research. Practical implications Publishing efficiency, as an indicator, shows how successful a city is at the production of science. Naturally, cities have limited opportunities to compete for components of the science establishment (e.g., universities, hospitals). However, cities can compete to attract innovation-oriented companies, high tech firms, and R&D facilities of multinational companies by for example establishing science parks. The positive effect of this process on the city’s performance in science can be observed in the example of Beijing, which publishing efficiency has been increased rapidly. Originality/value Previous scientometric studies have examined cities’ publication output in terms of the number of papers, or the number of highly cited papers, which are largely size dependent indicators; however this paper attempts to present a more quality-based approach.

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)0.004
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.019
Science and technology studies0.0010.001
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.099
GPT teacher head0.275
Teacher spread0.176 · 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 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

Citations9
Published2018
Admission routes1
Has abstractyes

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