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Record W7008728894

Conclusions: The prospects for ageing labour forces

2008· article· en· W7008728894 on OpenAlexaboutno aff

Bibliographic record

VenueSwinburne Research Bank (Swinburne University of Technology) · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsPopulation ageingRetirement ageDeveloped countryRestructuringPessimismPopulationQuarter (Canadian coin)Newly industrialized countryWelfare state
DOInot available

Abstract

fetched live from OpenAlex

Older workers have borne the brunt of industrialized nations' efforts to grapple with the effects of economic restructuring and population ageing. Although a trend towards early retirement has been a common feature of all the industrialized nations as industry restructured at the end of the twentieth century, the extent of this has varied markedly. This volume contains examples of where the participation of older workers declined, but not markedly so (Japan and the USA), and extreme examples of early exit (France, Germany and the Netherlands). But quickly, early retirement has been abandoned as its costs escalated, deficiencies were identified and new priorities associated with population ageing emerged. It is an unpalatable truth that many European governments in particular have been forced to accept that ageing populations and large scale early retirement are incompatible. Although early retirement is a tool that, it seems, industry defaults to, and while a period of almost a quarter of a century out of the workplace is attractive to many individuals, current thinking is that this is not tenable if industrialized economies are to remain competitive (European Commission, 2005a). The European Commission (2003) has estimated that an increase in the effective age of retirement of one year would reduce the expected increase in expenditure on public pensions by between 0.6 and 1 percentage points of GDP. The economic gains alone resulting from 'active ageing' could be enormous. However, there is some pessimism among authors in this volume, particularly those from countries where early exit went deepest, that active ageing is realizable, at least in the near future, and without the risk of hardship for older workers.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.006
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.218
GPT teacher head0.422
Teacher spread0.204 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2008
Admission routes1
Has abstractyes

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