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Record W4386775944 · doi:10.53379/cjcd.2023.374

Organizational Age Scale: New Lenses to Assess the Ageing of Workers

2023· article· en· W4386775944 on OpenAlexaffvenue
Amélie Doucet, Sophie Meunier, Lagacé Martine

Bibliographic record

VenueCanadian Journal of Career Development · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsUniversity of OttawaUniversité du Québec à Montréal
Fundersnot available
KeywordsObsolescenceScale (ratio)Context (archaeology)PsychologyAge discriminationPerceptionSocial psychologyGerontologyMarketingBusinessMedicineLabour economicsGeographyEconomics

Abstract

fetched live from OpenAlex

Few studies have focused on the aging process within the specific context of organizations (Thomas et al., 2014), due to a lack of adequate measures to assess who is an older worker and on what basis do we define such a worker. This paper introduces such a measure, namely the Organizational Age Scale (OAS) comprised of subjective age-related indicators stemming from the work context (Sterns & Doverspike, 1989; McCarthy et al., 2014; Kooij et al., 2008). More specifically, the OAS measures the individual’s perception of his-her own aging as a worker along five dimensions: obsolescence, age norms, career stage, time remaining in the workplace and opportunities for professional development. Such a tool helps identifies workers at risk of embodying negative age-based stereotypes and thus may counter the negative consequences that can result from self-ageism.

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)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.003
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.254
GPT teacher head0.367
Teacher spread0.113 · 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 designBench or experimental
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
Published2023
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Career DevelopmentSame topicRetirement, Disability, and EmploymentFrench-language works237,207