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Record W2115780921 · doi:10.1177/0164027505279720

Status Inequality and Occupational Regrets in Late Life

2005· article· en· W2115780921 on OpenAlexaff
Scott Schieman, Leonard I. Pearlin, Kim B. Nguyen

Bibliographic record

VenueResearch on Aging · 2005
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRegretPsychologyInequalityRace (biology)Educational attainmentWork (physics)Social psychologyLife course approachExperiential learningPrejudice (legal term)SociologyGender studiesPolitical science

Abstract

fetched live from OpenAlex

In a sample of adults aged 65 years and older, the authors examine structural and experiential sources of work-related perceived discrimination and regrets about occupational attainment. These appraisals are anchored in the circumstances of people’s lives, especially those linked to race and gender. Findings indicate that Black men report the highest levels of work-related discrimination, net of achieved statuses. Black women and men report higher levels of work-related regret than Whites. Compared with Whites, Black men’s disadvantages in achieved statuses like education, occupation, economic resources, and perceived work-related discrimination contribute to their higher levels of regret. Conversely, Black women would report more work-related regret were it not for the fact that their perceived work-related discrimination is relatively similar to that of White women and men. The authors’findings underscore the social-structural and life-course foundations of a critical—but understudied—outcome of self-evaluation processes in late life: the sense of regret.

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)0.007
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.431
GPT teacher head0.606
Teacher spread0.175 · 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

Citations15
Published2005
Admission routes1
Has abstractyes

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