MétaCan
Menu
Back to cohort

"Human Resource Approaches to Retirement: Gatekeeping, Improvising, Orchestrating, and Partnering"

2015· article· en· W4256392840 on OpenAlexaff
Mary Dean Lee, Jelena Zikic, Sung‐Chul Noh, Leisa D. Sargent

Bibliographic record

VenueAcademy of Management Proceedings · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHuman Resource and Talent Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsImprovisationWorkforceGatekeepingDemographicsProcess (computing)Human resourcesResource (disambiguation)BusinessQualitative researchAdaptation (eye)Human resource managementKnowledge managementPublic relationsMarketingSociologyManagementPsychologyEconomicsPolitical scienceEconomic growthComputer science

Abstract

fetched live from OpenAlex

This qualitative study examines the variation in HR approaches to retirement across 24 organizations, in order to explore innovative practices as well as gain understanding of the differences in how firms are dealing with major changes surrounding retirement and workforce demographics using organizational adaptation theory. Through careful analysis of in-depth interviews with HR managers, we identify three dimensions that differentiate organizations approaches to retirement: a) actions and interactions of key stakeholders in the retirement process; b) HR information gathering focus regarding workforce issues; and c) HR posture around changes needed in retirement policies and practices. Based on organizational profiles on these dimensions, four distinct approaches to retirement emerge and are described in some detail: Gatekeeping, Improvising, Orchestrating and Partnering. These different approaches provide insight into what organizations are doing and why, by examining how they differ in their adaptation to change.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.174
GPT teacher head0.272
Teacher spread0.098 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueAcademy of Management ProceedingsSame topicHuman Resource and Talent ManagementFrench-language works237,207