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Record W4308460204 · doi:10.26522/ssj.v16i3.2523

Critical Legal Practices: Approaches to Law in Contemporary Anti-racist Social Justice Struggles in Sweden

2022· article· en· W4308460204 on OpenAlexvenueno aff
Maja Sager, Marta Kolankiewicz

Bibliographic record

VenueStudies in Social Justice · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsAmbivalenceSociologyLawEconomic JusticeLegal professionIdeologyLegal realismRedistribution (election)Social movementPolitical scienceSocial psychologyPoliticsPsychology

Abstract

fetched live from OpenAlex

Based on interviews with legal practitioners working with or within anti-racist social justice movements in Sweden, we explore some dilemmas and paradoxes that appear when social movements pursue struggles for anti-racist social justice through the legal arena. How do the interviewees understand and critically relate to legal practices in contemporary anti-racist social justice struggles? What are the conditions of engagement of these organisations in the legal arena and how do they impact social justice struggles in Sweden? What are the stakes in the legal practices of these movements? Rather than a strategically chosen tool for social justice, legal practice could be understood as a kind of self-defence, as resorting to law is often a response to an unjust legal system, oppressive treatment by the state or disadvantage and deprivation. The interviewees’ reflections on their legal practices are informed by a fundamental ambivalence between the ideological commitment in the critique of law and their position from which it is impossible to ignore the legal arena. Instead of taking a clear stance for or against the law as a tool for social justice struggles, we have attempted to understand what are the methods and the effects of legal practice that grow from this ambivalence. The accounts of our interviewees indicate that both practical strategies and ways of accounting for these aim at subverting and challenging the law while at the same time using it. Throughout the analysis we have conceptualised these strategies as decentring, re-politicising and redistribution.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.830
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0050.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.346
GPT teacher head0.450
Teacher spread0.104 · 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 teacher head, not a consensus.

Study designQualitative
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

Citations3
Published2022
Admission routes1
Has abstractyes

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