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Making Equality Effective

2022· book-chapter· en· W4317369026 on OpenAlexaboutno aff
Sandra Fredman

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEnforcementLaw and economicsLegislationAgency (philosophy)Political scienceBusinessPublic relationsEconomicsLawSociology

Abstract

fetched live from OpenAlex

Abstract This chapter considers ways of making equality more effective by improving methods of enforcement and remedies. Section II assesses the individual complaints model, demonstrating its limitations as an engine for change. Reliance on a complaints model places an inordinate burden on individual victims and has little impact on systemic and structural discrimination. Section III examines ways in which the complaints model might be strengthened, through class actions, strategic litigation, and agency enforcement, and touches on how courts can be mobilized by social movements. Section IV turns to approaches which depart from the dependency on individual initiative and instead require proactive action from those in the best position to bring about change. It assesses several approaches, such as the public sector equality duty in the UK, Canadian employment equity legislation, and contract compliance in the US. Despite the great potential of proactive measures, in practice they easily slide into mere bureaucratic compliance. This section briefly attempts to explain these challenges in terms of different regulatory models, such as reflexive law, responsive law, and endogenous theories of law. The challenge remains to achieve the appropriate synthesis between harnessing the energy of responsible bodies to provide creative responses to systemic discrimination, and effective regulatory measures which can pierce the façade of compliance. Central to this is the need to involve those who are affected, as well as trade unions and civil society stakeholders, in defining the problem and in holding bodies to account.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.885
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0230.001

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 head0.079
GPT teacher head0.291
Teacher spread0.211 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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