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Record W2150580942 · doi:10.54648/ijcl2015002

The Decent Work Agenda and the Advancement of Gender Equality: For Emerging Economies Only?

2015· article· en· W2150580942 on OpenAlexaboutno aff
Fiona Macdonald, Sara Charlesworth

Bibliographic record

VenueInternational Journal of Comparative Labour Law and Industrial Relations · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsGender equalityFraming (construction)Political scienceInequalityWork (physics)Gender inequalityContext (archaeology)European unionCivil societyUnpaid workGovernment (linguistics)SociologyPolitical economyGender studiesEconomicsEconomic policyLawPolitics

Abstract

fetched live from OpenAlex

The International Labour Organization's Decent Work Agenda offers a valuable alternative to the traditional framing of most contemporary employment regulation. It moves beyond the standard employment relationship to include workers in non-standard employment and the attainment of gender equality has a central place, illustrated in the ILO's 2009 campaign around 'gender equality at the heart of decent work'. While most OECD countries have endorsed the Decent Work Agenda (DWA), few have taken it up at the domestic level, apparently seeing it as something of benefit to emerging economies only. Our article draws on interviews with key government, employer, union and civil society stakeholders in Australia, Canada, the Netherlands and the United Kingdom, and an analysis of relevant policy documents to tease out this 'othering' of the DWA and how different understandings of gender (in)equality relate to views about its utility in the national context. We argue that assumptions that the DWA has little to offer developed economies represent a missed opportunity to rethink the gendered policy underpinnings of domestic employment regulation that are shaped by and contribute directly to gender inequality.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.171
GPT teacher head0.401
Teacher spread0.230 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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