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Benchmarking in Human Resource Management

2009· article· en· W2139352455 on OpenAlex
Zhenjia Zhang, Qiu-mei Fan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian social science · 2009
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsBenchmarkingHuman resource managementHuman resourcesProcess managementKnowledge managementBusinessManagementComputer scienceMarketingEconomics

Abstract

fetched live from OpenAlex

How can a forward-thinking organization develop an effective performance-monitoring system in the area of human resource management has been a heated issue since early 1990s’. One of those approaches to HR performance monitoring is known as benchmarking. Benchmarking in Human Resource Management (HRM) has become an important issue to management. Although benchmarking has been approved one of the tools HR can employ to improve its ability to develop programs and initiatives that benefit the bottom line. Unfortunately, there are a number of misconceptions about the practice This paper introduces the definition of “Benchmarking”. Using literature review, survey, and figures to study and analyse the development of Benchmarking in HRM; its fitness into organizations’ operations; misconceptions and limitations about the Benchmarking in HRM; process of Benchmarking in HRM. Key words: Benchmarking, Human Resource Management (HRM), Performance monitoring, Organization Resume: Comment developper un systeme efficace de moniteur de performance dans le domaine de la gestion des ressources humaines pour une organisation prevoyante ? C’etait toujours une discussion passionnee depuis le debut des annees 90 . Une de ces approches pour le moniteur de performance des ressources humaines est celle de benchmarking . Benchmarking a la gestion des ressources humaines (HRM) est devenu une affaire importante pour la gestion . Benchmarking est considere comme un des outils des ressources humaines pour ameliorer ses capacites de developper des programmes et initiatives . Malheureusement , il y a bon nombre de malentendus sur la pratique . Ce texte presente la definition de “Benchmarking” . On etudie et analyse le developpement de Benchmarking a la gestion des ressources humaines a l’aide de la critique litteraire , de l’enquete et des figures . Ce texte essaye de montrer que Benchmarking est convenable pour les activites des organisations , et il presente egalement les opinions fausses et les limites sur le Benchmarking a la gestion des ressources humaines , ainsi que le processus de Benchmarking a la gestion des ressources humaines. Mots-cles: Benchmarking, gestion des ressources humaines, moniteur de performance, organisation

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.819
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.030
GPT teacher head0.338
Teacher spread0.308 · 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