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Record W4392066923 · doi:10.51644/9780889206199

Retirement

2006· book· en· W4392066923 on OpenAlexaboutno aff
Morris M. Schnore

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPolitical science

Abstract

fetched live from OpenAlex

Over the last twenty years in Canada there has been an increasing trend toward retirement at age sixty-five or earlier. Despite this trend, relatively few social scientists have studied the transition and consequences of retirement. The need of Canadian research regarding retirement is especially acute because the processes of retirement are culture-bound, and generalizations from data gathered in other countries may not be warranted. This study bridges that gap, providing a model for assessing subjective well-being among workers and retirees and thoroughly testing that model in the field. Many of the empirical findings of this study contradict predictions made on the basis of the "identity crisis" theory of retirement—that theory which posited retirement as a degrading experience, a self-destruction of the image the retiree once had of himself or herself as a worker and productive member of society. The indications of the empirical findings presented here are that, in fact, satisfaction with life, or some aspects of it, is commonly found to be high or higher among older adults than among the younger. In the course of the study, the author examines several different theories of adjustment to retirement, presents a model for measuring satisfaction among retirees more empirically than has been done, and suggests some of the policy decisions that might reasonably be proposed on the basis of the study's findings.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.669
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0870.036

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 head0.222
GPT teacher head0.420
Teacher spread0.197 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2006
Admission routes1
Has abstractyes

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