Towards gender equality in research and academia: conceptualising and practising inclusive mentorship through community building, co-creation and art-based methods
Bibliographic record
Abstract
Purpose This paper explores inclusive mentoring as a transformative tool for promoting gender equality in European academia and research. It reflects on the work of COST Action VOICES, which addresses systemic barriers and inequalities disproportionately affecting women and marginalised groups. The paper critically examines mentorship programmes, highlighting power imbalances, intersectionality and the role of participatory, co-creative and art-based approaches in reshaping mentoring practices. Design/methodology/approach Drawing on collaborative initiatives led by VOICES, the paper presents findings from an exploratory mentoring mapping exercise. Additionally, it reflects on the inclusive mentoring summer training held in Bilbao in 2024. Art-based methods and participatory co-creation workshops were employed to capture lived experiences of mentors and mentees, resulting in the development of ten guiding principles for inclusive mentoring. Findings The paper reveals significant inconsistencies in the availability, sustainability and evaluation of mentoring programmes across Europe. It identifies key challenges, including unequal access, lack of intersectional approaches and limited institutional support. Despite these barriers, inclusive mentoring models emphasising reciprocity and reverse mentoring show promise in enabling resilience, psychological safety and systemic change. The co-creation of guiding principles reflects a collective vision for mentoring as a tool for empowerment and solidarity. Originality/value This paper offers a novel contribution by integrating intersectional feminist approaches with art-based and participatory methods to redefine mentoring. It underscores the necessity of nurturing mentoring frameworks as dynamic ecosystems, requiring continuous care and collaboration to sustain progress in equality, diversity and inclusion.
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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 | 0.062 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.012 | 0.071 |
| Scholarly communication | 0.030 | 0.021 |
| Open science | 0.005 | 0.038 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".