Supporting Women in Geography (SWIG) Ireland: Confronting the role of gender and asserting the importance of the female voice
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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".