What Makes a Working Paper in Economics Publishable? A Tale from the Scientific Periphery
Bibliographic record
Abstract
Research on scientific production and publications in the field of economics has positively boomed in the last few years. However, hardly any attention has been dedicated to the production of working papers and the consequences they may have within the institutions where they are produced. This paper provides a detailed analysis of the working papers produced and published from an institution that is relatively peripheral in terms of its production of research in economics. It mainly explores the probability of the working papers being published in peer-reviewed journals. Through the use of an extensive series of these working papers, produced between 1985 and the end of 2005, and through the estimation of a logistic regression model, it was concluded that the probability of international publication increases significantly when the working paper is recent and co-written with a researcher from a foreign institution. Such evidence suggests that for success in the ‘publish or perish’ world of scientific research, one has to be integrated into an international scientific network.
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 | 0.086 | 0.366 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.026 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.035 | 0.033 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".