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Record W3178424575 · doi:10.1111/1468-2230.12671

Boycott, Resistance and the Law: Cause Lawyering in Conflict and Authoritarianism

2021· article· en· W3178424575 on OpenAlexfundno aff
Kieran McEvoy, Anna Bryson

Bibliographic record

VenueModern Law Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
FundersEconomic and Social Research CouncilQueen's UniversityQueen's University Belfast
KeywordsBoycottAuthoritarianismResistance (ecology)LawPower (physics)SociologyLegitimacyPoliticsPolitical scienceDemocracy

Abstract

fetched live from OpenAlex

Abstract This article examines the role of cause lawyers in conflicted or authoritarian contexts where the chances of legal victory are often minimal. Drawing upon the literature on resistance, performance, memory studies, legal consciousness and the sociology of lawyers, the paper examines how cause lawyers challenge and subvert power. The paper first explores the tactics and strategies of cause lawyers who boycott legal proceedings and the relationship between such boycotts and broader political struggles, legitimacy and law. It then examines why and how cause lawyers engage in fairly hopeless legal struggles as acts of instrumental resistance (the ‘sand in the cogs’), transforming courts into sites of symbolic resistance, and using law as a form of memory work. The paper argues that boycott of and resistance through the courts can counter the use of law as an instrument of wickedness and a tool of denial and preserves a ‘stubborn optimism’ in the rule of 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)0.006
metaresearch head score (Gemma)0.012
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: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.012
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.037
GPT teacher head0.315
Teacher spread0.278 · 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
GenreEmpirical

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

Citations7
Published2021
Admission routes1
Has abstractyes

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