Jurisdiction and Scale: Legal `Technicalities' as Resources for Theory
Bibliographic record
Abstract
Since the 1980s, critical studies of law and space have fruitfully explored the insight that law's mechanisms can be understood in part as mapping exercises. Existing work on law's scales (especially that using a post-colonial studies frame) has delved into the qualitative as well as the quantitative dimensions of scale, thus exposing some key epistemological issues in law. This article moves the discussion forward by demonstrating that theoretical work on `scale' — outside and inside legal studies — could benefit from studying specifically legal mechanisms such as `jurisdiction'. Recent work has shown that the various modes and rationalities of governance that coexist in every political-legal `interlegality' are not necessarily tethered to any particular scale; thus, exploring jurisdiction's effects takes us beyond scale. As an example, the knowledge moves that constitute what in the USA is called `the police power of the state' are briefly discussed. The fact that the gaze of police science/police regulation is not simply geographically local, but is rather specifically urban, shows the importance of understanding the complex governing manoeuvres enabled by the legal game of jurisdiction — especially if work on `scale' and jurisdiction is then supplemented by a consideration of the plural temporalities of governance, since temporality tends to become invisible both in analyses that privilege space and in the somewhat static diagrams of governance that make up the game of jurisdiction.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.114 |
| Scholarly communication | 0.013 | 0.037 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".