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Record W2247424333

Developments in the Irish labour market during the crisis: What lessons for policy?

2015· article· en· W2247424333 on OpenAlexaboutno aff
Thomas Conefrey, Martina Lawless, Suzanne Linehan

Bibliographic record

VenueArrow@dit (Dublin Institute of Technology) · 2015
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIrishUnemploymentEconomicsBustQuarter (Canadian coin)Financial crisisRecessionWageLabour economicsDemographic economicsKeynesian economicsBoomEconomic growthHistory
DOInot available

Abstract

fetched live from OpenAlex

This paper provides a comprehensive description of the evolution of the Irish labour market over the past fifteen years, with a particular emphasis on the crisis-related adjustment and its consequences. The sectoral aspect of employment developments and in particular the role of the construction sector is a common theme of this paper. With the unemployment rate tripling between 2008 and 2011, there have been clear implications for labour supply, as evidenced by the falloff in participation and the reversion to net outward migration. Micro data is used to calculate flows between labour market states, thereby providing insight into labour market dynamics. The issue of mismatch between labour demand and supply is highlighted as a key post-crisis challenge. A number of policy messages emerge from this analysis including the importance of a recovery in domestic demand for alleviating the unemployment problem.

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)0.006
metaresearch head score (Gemma)0.007
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 score0.087
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0030.005
Scholarly communication0.0140.011
Open science0.0030.006
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0210.004

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 head0.084
GPT teacher head0.406
Teacher spread0.322 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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