Bibliographic record
Abstract
Despite the many alternative insights produced within human geography since the height of the spatial science tradition of the 1960s and those within geographic information systems (GIS) itself, we still observe in our classrooms, hiring committees, and textbooks a dominant and singular understanding of GIS that fixes its meaning in ways that marginalize “non-GIS” geography. We are concerned about the effect that this valuation of GIS and devaluation of its others might have on the discipline of geography. In what follows, we report on our examination of the dominant discourse of GIS across a variety of sites in numerous academic, commercial, and educational sources where we found it to be repeatedly performed in ways that give particular meaning and power to “GIS.” We identify four characteristics attributed to GIS by and through this widespread discourse. We then discuss the effect of this discourse and, in particular, what it might mean to the discipline of geography. Finally, we suggest an exploration of “heterodox GIS” as a discursive strategy that we should deploy in our classrooms, departments, and beyond, as well as a political project aimed at destabilizing a singular and orthodox GIS. Such strategies should not strive to undermine or negate GIS but, rather, should aim to negate the notion that GIS is a single thing, linearly progressing, inherently expanding, and universally applicable.
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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.011 | 0.077 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| 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".