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Record W2921894939 · doi:10.1177/1035304619835075

A turning point for labour market policy in Australia

2019· article· en· W2921894939 on OpenAlexaff
Jim Stanford

Bibliographic record

VenueThe Economic and Labour Relations Review · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEconomicsLabour economicsPoliticsInequalityUnemploymentIndustrial relationsQuality (philosophy)Market economyPolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

Australian labour market and industrial relations policies are poised for fundamental change. A combination of political and macroeconomic factors has created a historic opportunity to turn away from the individualised, market-driven labour market policy that has prevailed since the 1980s, in favour of a more interventionist and egalitarian approach. Factors contributing to this moment include the breakdown of bipartisan consensus around key neoliberal precepts; growing public anger over inequality, insecure work and stagnant wages; and a weakening of macroeconomic conditions. Australia’s labour market is now marked by underutilisation of labour in various forms, a deterioration in job quality (especially the growth of insecure and precarious work) and unprecedented weakness in wages. The deterioration in job quality and distributional outcomes is the long-term legacy of the post-1980s shift away from Australia’s earlier tradition of equality-seeking institutional structures and regulatory practices. The current malaise in labour markets should be confronted with a comprehensive strategy to both increase the quantity of work available to Australian workers and improve its quality. The major components of such a strategy are identified, and their prospects considered, in light of the economic and political forces reshaping Australia’s labour market. JEL Codes: J28, J38, J53, J58, J83

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.079
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.023
GPT teacher head0.322
Teacher spread0.299 · 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 designObservational
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

Citations9
Published2019
Admission routes1
Has abstractyes

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