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Record W1832979462 · doi:10.26686/lew.v0i0.1256

The Use of New Technology and Rising Inequality in New Zealand: Evidence from Unit Record Data

2004· article· en· W1832979462 on OpenAlexaboutno aff
Chris Hector

Bibliographic record

VenueLabour Employment and Work in New Zealand · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsnot available
Fundersnot available
KeywordsInequalityUnit (ring theory)Quarter (Canadian coin)Distribution (mathematics)Demographic economicsWageEconomicsLabour economicsGeographyPsychology

Abstract

fetched live from OpenAlex

From the late 19' 11 century to the late 20'11 century inequality was generally in decline in all the developed countries, including New Zealand. However this pattern was abruptly reversed in the 1970s, and at least up to the mid 1990s inequality was generally on the rise again. The last quarter of the 2fl' century was also marked by rapid uptake of new information and communication technologies (JCT), prompting many commentators to ask whether there might be a connection. The present study uses unit record data from the Household Labour Force Survey to explore the extent to which wage inequality is related to new technology in New Zealand, and the extent to which it is correlated with skills and qualifications. The relationship appears to be relatively strong for workers in the lower half of the distribution, suggesting that workers with low skill levels have very poor prospects in industries using new technology. If the adoption of new technology is to be further encouraged it may be important to raise the skill levels of workers near the bottom of the distribution.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.110
GPT teacher head0.286
Teacher spread0.176 · 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 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

Citations1
Published2004
Admission routes1
Has abstractyes

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