The Case of Poland: Encouraging Human Geography through Lesbian Studies
Bibliographic record
Abstract
What follows is a direct transcription of the conference paper that I gave during the very prestigious Annual International Conference 2018 of the Royal Geographical Society (with the Institute of British Geographers). It took place at the Cardiff University, Wales, UK, on 28-31 of August, 2018. I gave this presentation within the very same panel session that I organised together with Kath Browne from the Maynooth University in Dublin, Ireland, and Catherine J. Nash from the Brock University in Canada. Our session, which I was also honoured to chair, was titled Engaging Contemporary Sexual-Gendered Realities: Geographies of Feminisms, Sexualities, and Beyond1 . Five presentations were part of the panel, including mine. The 20-minute paper was fortified with a PowerPoint presentation, which, of course, is irrelevant to the purposes of the transcription herein. Even though I have decided to keep the title of this transcription in compliance with the title of that presentation, its potential and meaning is much greater. Thinking of a proper academic article that I could develop based on that paper, I would probably make it a point to emphasise the significance of the intersections between sociology and geography, and how they affect my local ambitions to introduce (into the Polish academia) geographies of sexualities on the one hand and interdisciplinary lesbian studies on the other. The paper presented below reflects on the implications of this experience and stance of mine.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.027 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.001 | 0.025 |
| Research integrity | 0.003 | 0.006 |
| 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".