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Record W2126568036 · doi:10.1177/1038411108091755.

Human resource practices for mature workers -- And why aren't employers using them?

2008· article· en· W2126568036 on OpenAlexaff
Marjorie Armstrong‐Stassen

Bibliographic record

VenueAsia Pacific Journal of Human Resources · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsWorkforceBusinessHuman resource managementBridge (graph theory)Public relationsHuman resourcesMarketingManagementPolitical scienceEconomicsEconomic growthMedicine

Abstract

fetched live from OpenAlex

Two studies were conducted to assess the extent to which organizations were engaging in HR practices targeting mature workers and the reasons why organizations may not be engaging in these practices. The participants included 284 mature workers (171 in career jobs and 113 in bridge jobs) and 426 HR executives. Overall, organizations were reported to be engaging in the HR practices to a very limited extent. There were few significant differences between career-job and bridge-job respondents. Recognition and respect practices were rated as the most important HR strategy in influencing the decision to remain in the workforce. Over three-quarters of the mature workers indicated that organizations are not engaging in practices tailored to mature employees because it is not a priority for organizations whereas just over half of the HR executives indicated their organization was not engaging in these practices due to the lack of employee interest in, and demand for, such practices.

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.260
GPT teacher head0.423
Teacher spread0.163 · 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 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

Citations82
Published2008
Admission routes1
Has abstractyes

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