Addressing Gender-Based Violence through the ERA Policy Framework: A Systemic Solution to Dilemmas and Contestations for Institutions
Bibliographic record
Abstract
The consequences of gender-based violence in academic cultures are severe for individuals, the study and work climate, and for the quality of research and education. EU and national policy frameworks are developed since long, guiding academic institutions work on ending violence and abuse in the European Research Area (ERA). In this article, a critique and solution to specific dilemmas and contestations immanent in transforming ERA wide policy development into effective actions on the institutional level are presented. The analysis and policy input builds on extensive knowledge from long-term gender mainstreaming programs in national contexts, thorough experience from working amidst a research political landscape with conflicting academic, political, and bureaucratic paradigms, and research-based knowledge on policy development on gender-based violence. A core contribution from the article is the development of a generic, intermediating, and systemic institutional framework for implementation, acknowledging both the ERA policy developments and the day-to-day challenges on the institutional level, from the viewpoint of succeeding in ending gender-based violence in all ERA institutions. Also, a model for monitoring and evaluation of progress on the institutional level is proposed, accompanied by assessment criteria and a set of well-defined indicators. The proposed institutional framework can serve as an important step forward, in a collaborative effort among ERA stakeholders, and serve as inspiration for global academic institutions and national contexts to foster progress on the endemic of gender-based violence permeating academic communities.
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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 | 0.092 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.055 |
| Scholarly communication | 0.032 | 0.025 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.018 | 0.013 |
| 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".