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Record W2130068943 · doi:10.5539/jms.v2n2p158

A New Paradigm in Traditional Human Resource Management Practices

2012· article· en· W2130068943 on OpenAlexvenueno aff
Joe Eyo Duke, Ekpo Nya Udono

Bibliographic record

VenueJournal of Management and Sustainability · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHuman Resource and Talent Management
Canadian institutionsnot available
Fundersnot available
KeywordsHuman resource managementKnowledge managementHuman resourcesProductivityOrder (exchange)Competition (biology)Work (physics)BusinessGlobalizationOrganizational behavior and human resourcesResource (disambiguation)Paradigm shiftComputer scienceProcess managementOrganizational performanceEngineeringPolitical scienceManagementEconomics

Abstract

fetched live from OpenAlex

This paper identifies and proposes a number of approaches and practices that are designed to help organizations grapple with new work-place realities, the impact of globalization and international competition. The proposed measures signal a shift in some of the traditional human resource management practices, which are increasingly becoming inadequate. The measures are mainly focused on promoting new work-place cultures, organizational language, multi-skilling and customer focus. The researchers however conclude that a number of tested and established human resource management practices need to be combined with the new paradigm in order to achieve significant productivity improvements that can lead to widespread superior corporate performance. The study suggests further research of empirical flavour in order to establish the effectiveness of the commendatory propositions made.

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)0.030
metaresearch head score (Gemma)0.015
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: none
Teacher disagreement score0.030
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0070.062
Scholarly communication0.0160.021
Open science0.0040.008
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0040.001

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 head0.032
GPT teacher head0.270
Teacher spread0.238 · 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
Published2012
Admission routes1
Has abstractyes

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