Bibliographic record
Abstract
A mismatch between largely absolute Newtonian models of space in GIScience and the relational spaces of critical human geography has contributed to mutual disinterest between the fields. Critical GIS has offered an intellectual critique of GIScience without substantially altering how particular key geographical concepts are expressed in data structures. Although keystone ideas in GIScience such as Tobler's "First Law" and the modifiable areal unit problem speak to enduring concerns of human geography, they have drawn little interest from that field. Here, we suggest one way to reformulate the computational approach to the region for relational space, so that regions emerge not through proximity in an absolute space or similarities in intensive properties, but according to their similarities in relations. We show how this might operate theoretically and empirically, working through three illustrative examples. Our approach gestures toward reformulating key terms in GIScience like distance, proximity, networks, and spatial building blocks such as the polygon. Re-engaging the challenges of representing geographical concepts computationally can yield new kinds of GIS and GIScience resonant with theoretical ideas in human geography, and also lead to critical human geographic practices less antagonistic to computation.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.011 | 0.026 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".