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Record W629466054 · doi:10.1017/cbo9780511779640

Public Justice and the Anthropology of Law

2010· book· en· W629466054 on OpenAlexaff
Ronald Niezen

Bibliographic record

VenueCambridge University Press eBooks · 2010
Typebook
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsMcGill University
Fundersnot available
KeywordsInjusticeAppealPoliticsHuman rightsEconomic JusticeLawPublic opinionPolitical scienceRule of lawSociology

Abstract

fetched live from OpenAlex

In this powerful, timely study Ronald Niezen examines the processes by which cultural concepts are conceived and collective rights are defended in international law. Niezen argues that cultivating support on behalf of those experiencing human rights violations often calls for strategic representations of injustice and suffering to distant audiences. The positive impulse behind public responses to political abuse can be found in the satisfaction of justice done. But the fact that oppressed peoples and their supporters from around the world are competing for public attention is actually a profound source of global difference, stemming from differential capacities to appeal to a remote, unknown public. Niezen's discussion of the impact of public opinion on law provides fresh insights into the importance of legally-constructed identity and the changing pathways through which it is being shaped - crucial issues for all those with an interest in anthropology, politics and human rights law.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.009
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.037
Scholarly communication0.0090.012
Open science0.0010.003
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.034
GPT teacher head0.250
Teacher spread0.216 · 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 designTheoretical or conceptual
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

Citations89
Published2010
Admission routes1
Has abstractyes

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