Modelled worlds territory and the political geography of climate models
Bibliographic record
Abstract
Climate change has become a powerful incentive for political theorists, human geographers, and governments to rethink territory. Practitioners have argued in various manners that climate change poses challenges to the territorial state. For instance, the increasingly limited availability of natural resources, such as fresh water, threatens the livelihood of local communities which, in turn, heightens the risk of violent conflict. Some inlands and coastal areas become uninhabitable because of desertification and (periodic) flooding, leading to a rise in ecological refugees and, possibly, even entire refugee states. In addition, natural borders that run along rivers, watershed lines, coasts, and other natural structures, move, transform, or become indeterminate, engaging actors in new bordering practices. To name but a few examples. However, more than grappling with the effects of climate change on our political institutions, these shifting geographies call into question existing categories of space. In particular, climate change challenges the idea of solid, stable, dry land that underpins territory and related political-geographical notions of space. As such, climate is a problem of territory, as well as a territorial problem. In this thesis, I research the implications and complications of rethinking territory in the light of climate change. The notion of climate as both a scientific and a political concept and practice is based on numerical, computer-based climate models. For that reason, I analyse how ideas of space, place, and geography are conceptualised, defined, and operationalised in climate models, and I examine how these ideas relate to and/ or affect our political-geographical thought and practice. The question I answer is: What is the relation between territory and climate?
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".