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Record W1557991736 · doi:10.1108/hrmid-04-2014-0041

Melding a multi-generational workforce

2014· article· en· W1557991736 on OpenAlexaff
Peter Wesolowski

Bibliographic record

VenueHuman Resource Management International Digest · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHuman Resource and Talent Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsWorkforceOriginalityValue (mathematics)Public relationsBusinessMarketingWorkforce managementAging in the American workforceSociologyComputer scienceEconomicsEconomic growthPolitical scienceQualitative researchSocial science

Abstract

fetched live from OpenAlex

Purpose – Focuses on the potential advantages and pitfalls of a multi-generational workforce. Design/methodology/approach – Focuses on the potential advantages and pitfalls of a multi-generational workforce. Findings – Argues that younger people are often more technologically “savvy” than older employees and more at ease with open communication but that older employees also have a wealth of experience they can pass on to their younger colleagues. Practical implications – Demonstrates how organizations can use communication technology itself to bring the generations together. Social implications – Highlights how demographic factors are changing the nature of the workforce and moving the emphasis towards life satisfaction rather than simply career success. Originality/value – Reveals how new technology can help to solve some of the problems that the technology itself creates.

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)0.010
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.002
Scholarly communication0.0040.004
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.002

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 head0.034
GPT teacher head0.257
Teacher spread0.223 · 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
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

Citations11
Published2014
Admission routes1
Has abstractyes

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