Editorial Board Memberships: A Report on the Status of the Leading Information Systems Journals
Bibliographic record
Abstract
Research journals play a significant role in the generation, dissemination, and sharing of knowledge in an academic discipline. To a great extent, the editorial board members of these research journals manage and control the generation, dissemination, and sharing of knowledge. They also act as policymakers, gatekeepers, and trendsetters. In their latter roles, editorial board members can influence several factors in a discipline; namely, the research topics, the research methods, the research scope, and whose articles are published. The primary goal of this study is to investigate and report on the status of the editorial boards memberships in a set of 14 leading Information Systems (IS) journals. The study does this along the following three main diversity elements namely titles, gender, academic institution, and the geographical location of the editorial boards’ members. The set of 14 journals include the IS basket of 8 journal list. Of the 14 reviewed journals, 7 are domiciled in the US and 7 in Europe. Results reveal a lack of common editorial board classification criteria whereby members of the editorial boards were categorized into different groups and referred to using various titles such as senior editors, associate editors, editorial board members, editorial review board members, and board of editors. Also, the results show that, as of June 2020, the 14 IS journals’ editorial boards had 1214 instances (988 unique occurrences) of editorial board members who came from 44 unique countries. Of those 988 editorial board members, 253 (26%) were females while 736 (74%) were males. In addition, out of the 988 editorial board members, 48% were from US and Canada, 26% were from Europe, and 26% were from the rest of the world. The results also reveal the schools and faculty with the highest number of editorial board memberships. Having a significant number of editorial board members from US and Europe (74%) fits with what Kubota (2019) called epistemological racism; a practice in which the Western world has an upper hand in determining and controlling knowledge and academic practices. Given the roles of the editorial boards in the review process and setting the research agenda for a journal, a more diverse editorial board might publish a more diverse research output. Furthermore, a more diverse editorial board is likely to have a repertoire of internal reviewers who speed up and lower the review process costs, which are challenges inherent in a less diverse editorial board.
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 | 0.029 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".