For Anna: After critical GIS, what next?
Bibliographic record
Abstract
In honour of our lost colleague Anna K. Dvorak, we draw from elements of her last unfinished manuscript to explore new directions in critical GIS education and practice. Anna was a recent PhD in Geography hired into a critical GIS tenure‐track position. The ways in which she wove GIS practice through her research interests, teaching sensibilities, and community advocacy experiences defied easy categorization; we argue she represents a new generation of geography graduate student who is redefining where and how critical GIS education occurs. Anna's social and environmental justice work with the Pacoima Beautiful non‐profit organization in southern California formed the basis for an on‐going research initiative that gave her the opportunity to experiment with GIS as advocacy tool in the hands of local high school students. At the time of her death she had completed an initial draft of a manuscript situating this work in broader community activism issues. We remember Anna by presenting large sections of her work unaltered, interwoven with our commentary on the significance of her approach to critical GIS in a time of shifting academic and corporate commercial relationships to the technology.
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 | 0.012 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.022 | 0.032 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.032 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".