Bibliographic record
Abstract
Abstract This paper investigates the determinants of editorial board membership, for 17 leading journals in economics, from 1997 to 2009. We find that the researcher's scientific profile and connections to the editors in charge are significant predictors of editorship. Ceteris paribus, after controlling for unobserved researcher heterogeneity, scholars with links to editors in the co‐authorship network are more likely to serve as editors and this advantage decreases sharply with the social distance. Being a present or former departmental colleague or protégé of an editor‐in‐charge is positively associated with the probability of appointment to the board. Résumé Liens sociaux et comités éditoriaux en économie. Cet article explore les éléments déterminants relatifs à la composition des comités éditoriaux de 17 revues économiques de premier plan entre 1997 et 2009. Nous avons constaté que le profil scientifique du chercheur ainsi que ses relations avec les éditeurs augmentent la probabilité d’être membre d’un comité éditorial. Toutes choses étant égales par ailleurs, et après avoir neutralisé l’hétérogénéité non observée des chercheurs, il apparaît que les chercheurs en lien avec des éditeurs dans un réseau de corédaction sont davantage susceptibles de devenir éditeurs à leur tour, et que cet avantage s’amenuise drastiquement avec la distance sociale. Le fait d’avoir été collègue au sein d’un même département ou mentoré par un éditeur est associé de fac¸on positive à la probabilité d’intégrer le comité éditorial.
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 | 0.004 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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".