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Record W4415387026 · doi:10.1080/10438599.2025.2571618

Building a scientific community? The WOEPS workshop and the evolution of the economics of science, 1994–2023

2025· article· en· W4415387026 on OpenAlexaff
Daniel Souza, Aldo Geuna, Cornelia Lawson

Bibliographic record

VenueEconomics of Innovation and New Technology · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicUniversity-Industry-Government Innovation Models
Canadian institutionsCanadian Institute for Advanced Research
FundersEuropean Commission
KeywordsApplied economicsField (mathematics)Economic methodologySchools of economic thoughtScience communicationHeterodox economicsProcess (computing)Hard and soft scienceBusiness economicsBehavioral economics

Abstract

fetched live from OpenAlex

The paper studies the development of the Economics of Science as a new emerging field in the social sciences during the period 1994–2023. To identify the community of scholars working on this new scientific topic, we examine authors citing two seminal papers and use network analysis to investigate the cognitive and organizational characteristics of the community of authors. Our findings suggest that the Economics of Science is still in the process of becoming an independent and cohesive field, exhibiting a highly fragmented structure. We also study the role of the ‘Workshop on the Organisation, Economics, and Policy of Scientific Research’ (WOEPS), initiated in 2007, for the Economics of Science community. We show that WOEPS presenters have more economists of science as coauthors and are better positioned to connect different clusters of authors in the wider Economics of Science network than other members of the network, highlighting its importance for linking scholars in the field. We also show that WOEPS papers are published in higher ‘quality’ journals, receive relatively more citations, and significantly more citations from within the Economics of Science field compared to other Economics of Science papers.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.225
Teacher spread0.204 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2025
Admission routes1
Has abstractyes

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