GeoAI, counter-AI, and human geography: A conversation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This conversation inaugurates a new venture for Dialogues in Human Geography in which we host a discussion on topics of concern to our readers. Inspired by the underlying ethos of the journal as a place for dialogue, this is neither an interview nor an article, but rather an opportunity to bring together people with a range of views. In this discussion, we begin by tackling the issue of artificial intelligence and machine learning in geography, sometimes called GeoAI (geographic artificial intelligence). What is at stake with this development? We discuss how the legacy of the critical GIS movement, and specifically what Renée Sieber calls ‘counter-AI’, may yet have a role to play. For Krzysztof Janowicz, geographers are just getting started with GeoAI and many exciting developments lie ahead. Yet both sound a note of caution about data representation, bias, and blackboxing algorithms, as well as the need for accountability and how, ultimately, critique should be situated. The conversation took place in July 2022, and has been edited for clarity.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it