“I don't see gender”: Conceptualizing a gendered system of academic publishing
Bibliographic record
Abstract
Academic experts share their ideas, as well as contribute to advancing health science by participating in publishing as an author, reviewer and editor. The academy shapes and is shaped by knowledge produced within it. As such, the production of scientific knowledge can be described as part of a socially constructed system. Like all socially constructed systems, scientific knowledge production is influenced by gender. This study investigated one layer of this system through an analysis of journal editors' understanding of if and how gender influences editorial practices in peer reviewed health science journals. The study involved two stages: 1) exploratory in-depth qualitative interviews with editors at health science journals; and 2) a nominal group technique (NGT) with experts working on gender in research, academia and the journal peer review process. Our findings indicate that some editors had not considered the impact of gender on their editorial work. Many described how they actively strive to be 'gender blind,' as this was seen as a means to be objective. This view fails to recognize how broader social structures operate to produce systemic inequities. None of the editors or publishers in this study were collecting gender or other social indicators as part of the article submission process. These findings suggest that there is room for editors and publishers to play a more active role in addressing structural inequities in academic publishing to ensure a diversity of knowledge and ideas are reflected.
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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 | 0.038 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.017 | 0.105 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".