Integrating gender analysis into research: reflections from the Gender-Net Plus workshop
Bibliographic record
Abstract
Gender equality has been a crosscutting issue in Horizon 2020 with three objectives: gender balance in decision-making, gender balance and equal opportunities in project teams at all levels, and inclusion of the gender dimension in research and innovation content. Between 2017 and 2022, the EU funded, in collaboration with national agencies, 13 transnational projects under "GENDER-NET Plus" that explored how to best integrate both sex and gender into studies ranging from social sciences, humanities, and health research. As the projects neared completion, forty researchers from these interdisciplinary teams met in November 2022 to share experiences, discuss challenges, and consider the best ways forward to incorporate sex and gender in research. Here, we summarize the reflections from this workshop and provide some recommendations for i) how to plan the studies (e.g., how to define sex and/or gender and their dimensions, rationale for the hypotheses, identification of data that can best answer the research question), ii) how to conduct them (e.g., adjust definitions and dimensions, perform pilot studies to ensure proper use of terminology and revise until consensus is achieved), and iii) how to analyze and report the findings being mindful of any real-world impact.
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 | 0.151 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 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".