Gender and geographic representation in editorial boards of education journals
Bibliographic record
Abstract
Background Previous studies have examined the gender and geographic diversity within editorial boards across various academic disciplines, excluding the field of education. Thus, the purpose of this study was to address this gap by investigating the extent of gender and geographic disparity within the editorial boards of education journals. Methods The selection of top five education journals from each quartile (Q1–Q4) was performed based on Clarivate Analytics’ Journal Citation Reports (JCR) 2021 within the category of “Education & Educational Research.” The information of editors was collected through online sources. Results Overall, female editors accounted for 17 out of the 29 editors-in-chief (58.62%), 36 out of the 64 deputy/associate editors (56.25%), 378 out of the 728 editorial/advisory board members (51.92%) and 15 out of the 28 other types of editors (53.57%). There was no significant association between the impact factor (IF) and the proportion of female editors (Pearson’s r = −0.095, p = 0.689). The United States had the highest number of editors (n = 459, 54.06%), followed by the United Kingdom (n = 98, 11.54%), Australia (n = 63, 7.42%), China (n = 29, 3.42%), Germany (n = 25, 2.94%), and Canada (n = 22, 2.59%). Notably, the majority of the included editors were from developed countries (n = 794, 93.52%), while editors from developing countries constituted a significantly smaller proportion (n = 55, 6.48%). Conclusion Editorial boards of education journals exhibit reasonable gender diversity than other disciplines, though still fall short when considering the proportion of women in the discipline. Besides, obvious geographical disparity was observed among editorial boards of education journals. There was a notable lack of representation of researchers associated with institutions from developing countries on the editorial boards of education journals. While maintaining sufficient gender diversity, it is imperative to enhance the geographical diversity in these journals, ensuring a more equitable number of positions to individuals from these underrepresented groups.
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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".