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Record W1998188364 · doi:10.1177/001979390305600409

Work History and Later-Life Labor Force Participation: Evidence from a Large Telecommunications Firm

2003· article· en· W1998188364 on OpenAlexaboutno aff
Gangaram Singh, Anil Kumar Verma

Bibliographic record

VenueIndustrial and Labor Relations Review · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Labour economicsDemographic economicsPart-time employmentSurvey data collectionWork hoursEconomicsWorking hoursEngineering

Abstract

fetched live from OpenAlex

This study examines the relationship between later-life labor force participation and work history. Survey data on 1,805 Bell Canada early retirees show that 40% returned to work, of whom 17% took full-time employment, 51% took part-time employment, and 32% became self-employed. Return to work was positively related to work attachment and tenure in the last job, and negatively related to having been in a non-managerial occupation and lacking upward career mobility. Those with high attachment to work (as measured by responses to several survey questions) were more likely to return to full-time employment than to retire. Clerical workers were less likely than managers to choose part-time employment over retirement. Both lateral (versus upward) mobility in the last job and high work attachment were negatively related to the choice of self-employment over retirement.

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)0.001
metaresearch head score (Gemma)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.248

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.383
GPT teacher head0.431
Teacher spread0.048 · 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

Citations40
Published2003
Admission routes1
Has abstractyes

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