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Record W4308099605 · doi:10.1371/journal.pone.0276840

From art to science: A bibliometric analysis of architectural scholarly production from 1980 to 2015

2022· article· en· W4308099605 on OpenAlexafffund
Jean-Sébastien Sauvé, Philippe Mongeon, Vincent Larivière

Bibliographic record

VenuePLoS ONE · 2022
Typearticle
Languageen
FieldNeuroscience
TopicAesthetic Perception and Analysis
Canadian institutionsUniversité du Québec à MontréalDalhousie UniversityUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of CanadaCanada Research Chairs
KeywordsDirectoryArchitectureAnalyticsBibliometricsComputer scienceData scienceProduction (economics)World Wide WebSociologyLibrary scienceHistory

Abstract

fetched live from OpenAlex

According to recent literature on "architecture" as a discipline, practical knowledge relevant to its process of making has decreased in importance in favor of a more academic approach. Using data derived from Ulrich's Periodical Directory and Clarivate Analytics's Web of Science, this paper suggests providing empirical evidence supporting of such shift, as revealed by an overview of the dissemination practices in architecture scholarly production between 1980 and 2015. Our results support that architecture is becoming increasingly academic, as demonstrated by the growing proportion of articles and journals intended for scholars rather than for professionals. We also show that architecture is increasingly global, with decreased interest in local and/or national issues and the growing prevalence of English as a publication language. Finally, this academic focus is manifested in references cited by architectural papers with the gradual substitution of professional and artistic oriented knowledge, for scientific approaches tied to engineering and technology.

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)0.006
metaresearch head score (Gemma)0.040
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 score0.850
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.1500.194
Science and technology studies0.0020.002
Scholarly communication0.0070.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.081
GPT teacher head0.303
Teacher spread0.223 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

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