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Record W3134554617 · doi:10.5430/jms.v12n1p1

Employee Resource Groups (ERGs) – An Under-Utilized and Under-Organized Way of Developing New Staff, Management and Organizational Culture

2021· article· en· W3134554617 on OpenAlexvenueno aff
Alexey Kirilin, Keijo Varis

Bibliographic record

VenueJournal of Management and Strategy · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Learning and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsKnowledge managementSoft skillsCompetence (human resources)Human resource managementOrganizational cultureProcess (computing)PsychologyBusinessComputer sciencePublic relationsSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

We evaluated impact of employees’ participation in an employee resource group (ERG) on their soft skills learning and development and proposed a tool for assessing competence development that is often difficult to quantify.ERG has a positive effect on soft skills development, however certain soft skills do not develop in the interaction between management and new employees. Some soft skills require focused and organized learning.The role of ERG stretches beyond integration of new employees only. We propose a multidimensional paradigm where the management and organizational culture are considered as the object of development and new employees facilitate this process.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.030
GPT teacher head0.236
Teacher spread0.206 · 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
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

Citations2
Published2021
Admission routes1
Has abstractyes

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