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Record W1592730654

Reducing Group-based Inequalities in a Legally Plural World

2010· article· en· W1592730654 on OpenAlexaff
Colleen Sheppard

Bibliographic record

VenueSSRN Electronic Journal · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsMcGill University
Fundersnot available
KeywordsEnforcementPluralHuman rightsNormativePolitical scienceLaw and economicsInequalityLegal pluralismInternational lawInternational human rights lawLawSociologyLegal researchLegal realism
DOInot available

Abstract

fetched live from OpenAlex

We live in a world characterized by multiple, overlapping and plural normative orders embracing formal and informal legal regimes, customs and practices. Legal protections for equality and protections against discrimination are found in a plurality of legal instruments, including international, regional, national, state and municipal human rights documents and institutional codes of conduct. Moreover, formal equality rights operate in social and cultural contexts that are deeply influenced by the customs, norms and social practices of everyday life. In assessing how law may be used to reduce group-based inequalities, therefore, it is critical to examine the interaction between different sources of formal human rights protection and diverse, overlapping and coexisting social and cultural orders – or regimes of informal law. Such an exploration provides important insights into systemic, structural and social obstacles to effective enforcement of formal anti-discrimination and equality rights protections – obstacles institutionalized and embedded in both official and unofficial law and custom. Moreover, an appreciation of the intersections and interactions between a plurality of legal orders (both formal and informal) illuminates how strategic reliance on different sources of protection may advance the effective enjoyment of the right to equality. In this paper, I highlight how the plurality of law affects equality rights in institutional, community and global contexts.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.018
GPT teacher head0.285
Teacher spread0.267 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2010
Admission routes1
Has abstractyes

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