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Record W1528047166 · doi:10.26686/lew.v0i0.2209

The State of the New Zealand Labour Market

2015· article· en· W1528047166 on OpenAlexaboutno aff
David Paterson

Bibliographic record

VenueLabour Employment and Work in New Zealand · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsUnemploymentLabour economicsInflation (cosmology)Quarter (Canadian coin)PopulationWageChristian ministryLabour supplyUnemployment rateWage growthWorking populationDemographic economicsGeographyPolitical scienceEconomic growthDemography

Abstract

fetched live from OpenAlex

This paper will review recent developments in the New Zealand labour market and trace the passage of these indicators through the global financial crisis to the outlook for the coming 3 years. The paper is based on the Ministry’s Quarterly Labour Market report and Short-term Employment Forecasts. The paper describes a strong labour market. Indicators of labour demand growth have moderated from the elevated levels recorded earlier in 2014, but remain solid. Construction is a significant source of employment demand across the entire country, and not just Canterbury. Migration-led population growth and near-record labour force participation rates are expanding labour supply. Women in general are showing increased involvement in the labour market: the female labour force participation rate returned to its record high of 63.7 per cent (equal to that recorded in March 2014), and the female employment rate (59.7 per cent) is at its highest rate since December 2008. Single mothers in particular have seen a sharp increase in their employment rate, which has reached its highest level since the series began in 1986. High participation is likely slowing the fall in the unemployment rate, which nevertheless hit its lowest level since March 2009. Wage growth remains subdued over the September quarter, but this comes against the backdrop of low inflation.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.580
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.025
GPT teacher head0.309
Teacher spread0.284 · 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 designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

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