MétaCan
Menu
Back to cohort
Record W3177334178 · doi:10.5430/bmr.v10n2p1

Six Myths of Human Resource Management

2021· article· en· W3177334178 on OpenAlexvenueno aff
Cam Caldwell, Verl Anderson

Bibliographic record

VenueBusiness and Management Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsHuman resource managementAccountabilityMythologyContext (archaeology)Human resourcesBusinessPublic relationsWork (physics)Political scienceManagementEconomicsLawEngineering

Abstract

fetched live from OpenAlex

Introduction: For decades the Human Resource Management (HRM) strategic role has been viewed as limited in its effectiveness. Human Resource Professionals (HRPs) have been criticized for their lack of formal preparation – when that preparation even exists. According to Gomez-Mejia (2015) and colleagues, fewer than one-third of all HRPs have any academic preparation in HRM and most lack an understanding of the legal, professional, and technical principles of their profession.Objective: The purpose of this paper is to focus on six myths about HRM and the role of HRPs that are frequently held by Top Management Team (TMT) members, business practitioners, and even many HRPs. The failure of so many HRPs and TMTs to recognize the fallacies in these myths is a cause of organizational dysfunction and undermines the ability of HRPs to be ethical stewards who serve and protect the best interests of their organizations.Methods: The paper begins by briefly identifying seven key strategic functions of HRM in distinguishing the HRPs’ staff role in successful organizations.Results: We describe the context of today’s challenging work environment in which HRPs perform their labors and identify six myths that plague the HRM profession and undermine the effectiveness of many modern organizations.Conclusions: We conclude the paper with a challenge to TMT members and HRPs to raise the bar and increase the accountability of those who deliver HRM services within their organizations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.904
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.093
GPT teacher head0.388
Teacher spread0.295 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueBusiness and Management ResearchSame topicEconomic and Technological Developments in RussiaFrench-language works237,207