Bibliographic record
Abstract
Abstract This paper provides an overview of the research techniques that can be used for explorations in legal geography, highlighting the multiple instruments available in the legal geographer's methodological toolkit. These diverse methods stem from a twofold shift away from the ‘ordinary’ research techniques of human geography. This shift has entailed first, the adaptation of traditional qualitative methods, such as ethnography or interviews, to research on subjects like judges, politicians, and other elite members; and second, the appropriation of methods prevailing in the field of law, such as doctrinal analysis. Against this background, the paper shows which research methods can be used to investigate the different subdomains of the law‐space tangle (i.e., law‐in‐books, law‐as‐a‐system‐of‐practices, and experiencing‐the‐law). Among these methods, special attention is paid to doctrinal analysis, which is usually distant from the typical training of geographers: its characteristics and the caution required in its use are emphasised, as are the tools that can make it more systematic and the specific contribution that a geographical approach can make to it. The paper also discusses the possibility of using quantitative techniques, which are currently approached with a certain scepticism, to carry out legal geographical analyses.
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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 | 0.097 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".