Bibliographic record
Abstract
In the last decade, conversations around queering of GIScience emerged. Drawing on literature from feminist and queer critical GIS, with special attention to the under‐examined political economy of GIS, I suggest that the critical project of queering all of GIS, both GIScience and GISystems, requires not just recognition of the labour and lives of queers and research in geographies of sexualities. Based upon a queer feminist political economic critique and evidenced in my teaching critical GIS at two elite liberal arts colleges, I argue that the “status quo” between ESRI and geography as a field must be interrupted. Extending a critical GIS focus beyond data structures and data ethics, I argue that geographic researchers and instructors have a responsibility in queering our choice and production of software, algorithms, and code alike. I call this production and choice of democratic, accessible, and useful software by, for, and about the needs of its users, good enough software.
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 | 0.055 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.041 |
| Scholarly communication | 0.024 | 0.037 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".