Building a scientific community? The WOEPS workshop and the evolution of the economics of science, 1994–2023
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".