Discipline all the way down: Law and capital in Shaina Potts’ <i>Judicial Territory</i>
Bibliographic record
Abstract
This commentary engages Shaina Potts’ Judicial Territory as a significant methodological contribution to economic geography – one that draws crucial attention to the (often opaque) relationship between law and capitalism and provides geographers with a set of tools to penetrate it. Highlighting three key elements of Potts’ approach to the study of capitalist sociospatial relations, I explore how her focus on the law makes visible actors, logics and modes of political and economic discipline that have largely remained hidden from view in economic geography. First, Potts frames law as a ‘structuring link’ between capitalism and imperialism, developing a theoretical perspective that sheds light on the legal production of uneven development and social difference. Second, she outlines a method of ‘geographically relational’ legal analysis that offers practical lessons for incorporating law into political-economic investigations. And third, through her analytical focus on episodes of legal struggle and contestation, Potts reveals how diverse sets of forces, actors and motivations come together to produce legal change. Here, Judicial Territory highlights the complexity of legal transformation and its relationship to capitalist globalization without losing sight of a basic motivation behind it: the drive to discipline Third World states and repress non-capitalist forms of economic life.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".