GeoAI, counter-AI, and human geography: A conversation
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.
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 | 0.047 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.055 |
| Scholarly communication | 0.022 | 0.036 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.020 | 0.029 |
| Insufficient payload (model declined to judge) | 0.007 | 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".