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Record W2961368635 · doi:10.5334/pb.472

The Influence of Multiculturalism and Assimilation on Work-Related Outcomes: Differences Between Ethnic Minority and Majority Groups of Workers

2019· article· en· W2961368635 on OpenAlexaff
Patrizia Villotti, Florence Stinglhamber, Donatienne Desmette

Bibliographic record

VenuePsychologica Belgica · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsInstitut Universitaire en Santé Mentale de QuébecUniversité du Québec à Montréal
Fundersnot available
KeywordsEthnic groupMulticulturalismPsychologySocial psychologyMinority groupImmigrationMediationCultural assimilationAssimilation (phonology)Identity (music)Cultural diversityDiversity (politics)Work (physics)SociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

This study aims at acquiring knowledge on how to manage ethnic diversity at work in order to promote work-outcomes in minority and majority groups of workers. We tested a model on how assimilation and multiculturalism, endorsed at an organizational level, predict job satisfaction and intention to quit through a mediation role played by the identification of workers with both the organization and their ethnic group simultaneously (i.e., dual identity). We hypothesized that the indirect effects of multiculturalism on work outcomes via dual identity are stronger for minority and those of assimilation are stronger for majority. Data came from 261 employees who responded to an online survey. 77 were of foreign origin (minority group) and 184 were of Belgian origin (majority group). Both assimilation and multiculturalism relate positively to work-related outcomes for both groups. However, multiculturalism through dual identity has the most beneficial outcomes for workers of the minority group. Our findings highlight the need to take ethnic and identity issues in account when studying work outcomes in culturally diverse 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.114
GPT teacher head0.343
Teacher spread0.229 · 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 designObservational
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

Citations7
Published2019
Admission routes1
Has abstractyes

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