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Record W1926530704 · doi:10.26522/ssj.v3i2.1013

Older Workers in Changing Social Policy Patterns

2010· article· en· W1926530704 on OpenAlexvenueno aff
Nathalie Burnay

Bibliographic record

VenueStudies in Social Justice · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsPensionWorkforceGovernment (linguistics)UnemploymentMandatory retirementLabour economicsPublic policyRetirement ageAging in the American workforceSocial policyBusinessEconomicsEconomic growthMarket economyFinance

Abstract

fetched live from OpenAlex

Compared to other European countries, the employment rate of older workers in Belgium is rather low. This paper argues that one of the most relevant factors underlying the problems of this low employment rate in Belgium is the social policies directed at older workers. Indeed, when unemployment became a widespread phenomenon in the1970s and 80s, early-retirement schemes were designed to alleviate the financial implications on an aging workforce. The government encouraged anyone over 50 to leave the labour market through early retirement schemes, unemployment payment programs, medical retirement, and career breaks. These practises were based on a wide consensus of government, business, and workers.However, for some years now, international organizations have been concerned about the viability of pension systems and their ability to achieve their objectives. In recent years, different factors have led policy makers to rethink this policy. But changing the trend and keeping people on the job has proven more difficult than foreseen. The transformations of public policies begun at the dawn of the 21st century radically changed the balance between the state, workers, and employers, who had all previously seen early retirement as favourable. This paper also tries to show how early retirement is not simply a desire to escape, but can also be explained as an aggression against the person by the labour market. Leaving professional life early thus seems more to be a case of necessity, in fact not a choice at all, but an obligation, or even a sacrifice, and must be seen in the perspective of professional duties and their evolution.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.229
GPT teacher head0.506
Teacher spread0.277 · 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.

Study designQualitative
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

Citations2
Published2010
Admission routes1
Has abstractyes

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