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Record W4390660273 · doi:10.47260/amae/1423

Assessment of Employees’ Perception of Workplace Diversity and its Influence on Job Satisfaction: Insights from Calgary Economic Region

2024· article· en· W4390660273 on OpenAlexaboutno aff
Nkiru Sochi-Iwuoha, Okechukwu Sunday Abonyi, Deanne Larson

Bibliographic record

VenueAdvances in Management and Applied Economics · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsJob satisfactionDiversity (politics)PerceptionPsychologyJob attitudeSocial psychologyCultural diversityMarital statusGender diversityEthnic groupMulticulturalismSexual orientationJob performancePolitical scienceBusinessSociologyPopulation

Abstract

fetched live from OpenAlex

Abstract The Worlds constantly changing work environment and the continual increase of multiculturalism, diversity, and inclusion have become an important issue among Human Resources practitioners and business leaders. Few studies have focused on how an employee’s perception of diversity impinges on their job satisfaction. This study quantitatively investigated how employees perceive workplace diversity and the inherent effects of such perceptions on their job satisfaction. Survey data was collected from 430 employees in the Calgary Economic region. The results showed that age is not a significant factor in employees’ perception of workplace diversity. However, their level of job satisfaction differed significantly. The results also showed that employees’ perception of workplace diversity and job satisfaction differed significantly based on sexual orientation, level of education, and ethnic origin. On the other hand, employees’ perceptions of workplace diversity and job satisfaction did not differ significantly based on gender and marital status. The study further revealed that the perception of workplace diversity programs is positively related to job satisfaction. Keywords: Workplace Diversity, Job Satisfaction, Motivation, Calgary Economic Region, Social Identity.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.030
GPT teacher head0.274
Teacher spread0.244 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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