III The feminist fork in the road: Focusing on mentoring as support and strategy in the absence of magical insights for getting more men ‘on board’
Bibliographic record
Abstract
In this collection, several authors – ranging from early career to well established academics – consider the role of women and the female voice in academia. This compilation developed from a conference session organised by the Supporting Women in Geography (SWIG) Ireland group, at the Conference of Irish Geographers in University College Cork (UCC) in 2017. In the first piece, Ahern and Mc Ardle consider why this discussion is necessary at all, ruminating on examples from both within and outside of academia. Till then brings in her experience working in Ireland, the US and beyond, and reflects on the importance of including all voices, and challenges scholars to end gender discrimination in Ireland. Manzo then reflects on how female work in academia, similar to community organising, can be considered invisible, devalued labour (Daniels, 1987). Yet she focuses on the positives of this, outlining the women-centred community organising model, the social capital that is involved, and the range of activities for empowering women to alter the efforts in Irish academia to making this change. Meletis then widens this discussion with an international example of a group similar to SWIG Ireland, Inspiring Women Among Us (IWAU) in Canada. She reflects on the difficulty of being an inclusive group. These discussions are vital to tackling gender bias in Irish academia, yet all the authors agree this needs to be an ongoing conversation, a lived practice, and we hope this work inspires further contributions to this cause.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.024 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".