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Capabilities and Age Discrimination

2019· book-chapter· en· W2945188615 on OpenAlexaff
Pnina Alon-Shenker

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDignityAction (physics)Relevance (law)Value (mathematics)IntersectionalitySociologyPositive economicsLaw and economicsPolitical scienceGender studiesEconomicsLawComputer science

Abstract

fetched live from OpenAlex

Abstract This chapter explores the particular relevance and usefulness of the capabilities approach (CA) to age discrimination in the workplace. It argues that the CA provides a fresh theoretical framework for addressing significant barriers experienced by older workers in the labour market. First, the obstacles to attaining and maintaining decent work posed by ageism are examined against Sen’s idea of the real substantive freedom to lead lives we have reason to value and Nussbaum’s idea of core entitlements implicit in the notion of life worthy of the dignity of a human being. Second, the CA provides a strong critique of the popular view that age discrimination is a reasonable way of ordering our society by distributing benefits and burdens over a lifespan. Third, the CA’s sensitivity to variations in need highlights heterogeneity within the group of older workers and advances the consideration of intersectionality issues. Finally, the CA is useful in justifying positive action policies that shift responsibility back to governments.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.078
GPT teacher head0.377
Teacher spread0.300 · 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 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

Citations1
Published2019
Admission routes1
Has abstractyes

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