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Legal Geography

2017· other· en· W4241628670 on OpenAlexaff
Benjamin Forest

Bibliographic record

VenueInternational Encyclopedia of Geography · 2017
Typeother
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsMcGill University
Fundersnot available
KeywordsJurisdictionEmpirical legal studiesScholarshipDisciplinePolitical scienceCritical geographyCultural geographyLegal researchSpace (punctuation)Legal professionLegal pluralismJurisprudenceLegal realismLawSociologyHuman geographySocial science

Abstract

fetched live from OpenAlex

Legal geography, a highly fragmented subfield, is defined by formal attention to the spatial aspects of law and the legal dimensions of space and place. (Indeed, one may easily write of legal geographies.) Most work in the area concerns issues of jurisdiction, property, public/private space, race, and/or rights, although there is considerable variation in how these concepts are addressed. The majority of research is on common law systems, particularly in the United States, although there is a degree of attention to non‐Western and informal legal systems as well. The scholarship on legal and geographic issues outside common law systems and in the developing world tends to be categorized as legal anthropology or sociology, with relatively weak disciplinary links to geography. This separation, however, is generally institutional rather than intellectual, and there are numerous opportunities for greater cross‐disciplinary work. Much of the scholarship in legal geography is from a critical perspective, seeking to challenge established legal (or sociolegal) practices and to highlight the inequalities created or reinforced by and through them.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.123
Threshold uncertainty score0.411

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0030.005
Scholarly communication0.0090.005
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1230.030

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.

Opus teacher head0.007
GPT teacher head0.280
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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