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Record W1985007693 · doi:10.1080/09692290.2011.603668

The Jobs Strategy: From neo- to inclusive liberalism?

2011· article· en· W1985007693 on OpenAlexaff
Rianne Mahon

Bibliographic record

VenueReview of International Political Economy · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsBalsillie School of International AffairsWilfrid Laurier University
Fundersnot available
KeywordsFlexicurityNeoliberalism (international relations)Lisbon StrategyPoliticsLiberalismEuropean unionEconomicsPolitical economySocial securityEuropean social modelPolitical scienceSociologyEconomic systemMarket economyEconomic policyLaw

Abstract

fetched live from OpenAlex

This paper focuses on the OECD's Strategy, which became a principal means for the OECD to promote a neo-liberal model of social and employment policy to its member states and it also influenced the European Union's employment strategy. Yet important modifications were introduced in 2006 with the recognition of an alternative policy path, ‘flexicurity.’ While the latter accepted neoliberal macroeconomic ‘fundamentals,’ it broke with neoliberalism to the extent that it recognized a positive role for labour market regulations and social policies in promoting security and social inclusion. The paper analyses the politics associated with this shift, using the concept of ‘organisational discourse’ to get at the dynamic (and contested) nature of the OECD's ideational lens. While the OECD came under pressure from some of its member states to alter its ‘one size fits all’ strategy, the Economics Department, whose version of the discourse initially dominated, was also challenged from within by the Directorate for Employment, Labour and Social Affairs (DELSA), which developed its own alternative discourse.

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)0.009
metaresearch head score (Gemma)0.005
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 score0.019
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.032
Scholarly communication0.0190.008
Open science0.0010.007
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.049
GPT teacher head0.379
Teacher spread0.330 · 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

Citations18
Published2011
Admission routes1
Has abstractyes

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