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

Factors Influencing Cities’ Publishing Efficiency

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

Notice bibliographique

RevueJournal of Data and Information Science · 2018
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueSpatial and Panel Data Analysis
Établissements canadiensnon disponible
Organismes subventionnairesEuropean Social FundEuropean Commission
Mots-clésPublishingBeijingRegional scienceQuality (philosophy)Library sciencePolitical scienceChinaGeographyComputer scienceLaw

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,029
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesBibliométrie
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,993
Score d'incertitude au seuil0,021

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,029
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0070,019
Études des sciences et des technologies0,0010,001
Communication savante0,0060,002
Science ouverte0,0010,002
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,099
Tête enseignante GPT0,275
Écart entre enseignants0,176 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations9
Publié2018
Routes d'admission1
Résumé présentoui

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