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Reforming Age Discrimination Law

2022· book· en· W4293753904 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicDiscrimination and Equality Law
Canadian institutionsnot available
Fundersnot available
KeywordsStatutory lawEnforcementScholarshipNegotiationAge discriminationPolitical scienceLabour lawLawEmployment discriminationLaw enforcementLaw and economicsSociology

Abstract

fetched live from OpenAlex

Abstract Age is a critical issue for labour market policy. Both younger and older workers experience significant challenges at work. Despite the introduction of age discrimination laws, ageism remains prevalent. This book offers a roadmap for the future development of age discrimination law in common law countries, to better address workplace ageism. Drawing on theoretical, doctrinal, and empirical legal scholarship, and comparative perspectives from the United Kingdom, Australia, and Canada, the book provides a grounded critique of existing age discrimination laws and their enforcement, and puts forward concrete suggestions for legal reform and change. It examines the challenges and limitations of existing legal frameworks and the individual enforcement model for addressing age discrimination in employment, mapping the stages of claiming, negotiation, or alternative dispute resolution, and hearing and judgment, using mixed method case studies of the enforcement of age discrimination law in the United Kingdom and Australia. The book puts forward a fourfold model of reform to strengthen age discrimination law, to improve the individual enforcement model, strengthen positive equality duties, bolster the roles of statutory equality agencies, and enhance collective enforcement. The book critically considers how these options might address the limits of existing laws, and the practical measures necessary to ensure their success.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.759
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0160.000

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 head0.078
GPT teacher head0.358
Teacher spread0.280 · 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

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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