Editorial Board Diversity at the Basket of Eight Journals: A Report to the College of Senior Scholars
Bibliographic record
Abstract
At the 2019 International Conference on Information Systems (ICIS), the College of Senior Scholars appointed a committee to investigate diversity in the editorial boards of their Basket of Eight journals. Editorial board diversity signals that a journal welcomes and includes all authors. The committee compared the gender, regional and ethnic diversity of the editorial boards to that of AIS members in the Academic membership category. This comparison showed that the editorial boards overall had fewer female members, more members from Region 1, and fewer from Region 3 than one would reasonably expect. Furthermore, several ethnicities appeared on the boards in smaller or larger numbers than one would expect, in comparison to their proportions among AIS Academic members. The individual journals also differed a great deal among themselves with respect to these diversity criteria. Regrettably, every journal fell below what one would reasonably expect with respect to either gender, regional, or ethnic diversity. Based on these findings, we make recommendations for the College of Senior Scholars, editors in chief of the Basket of Eight journals, the AIS Council, and individuals who lead other organizations of IS scholars.
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 | 0.074 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".