The Effects of Work Force Diversity on Employee Performance in Singapore Organisations
Bibliographic record
Abstract
Workforce diversity has been identified as one of the strategic capabilities that will add value to the organizations over their competition. As Singapore is one of the most globally competitive countries, it attracts highly skilled and extremely innovative people to work here. Age, gender and ethnicity are the most commonly diversified demographic variables observed among the workforce of many organizations. Thus, the present study focuses on the effect of the workforce diversity in terms of age, gender and ethnicity. If the diversity of the workforce is properly managed, it will provide positive benefits. If not properly managed, however, it could lead to negative results. A self-administered questionnaire was used to collect the views of employees in both the manufacturing as well as the service industries in Singapore. The reliability of the survey was tested by estimating Cronbach’s alpha. The empirical relationship of age, gender and ethnicity of the employees with the performance was computed using Software Package for Social Science (SPSS). The analysis reveals that the three variables do not have a statistically significant impact on the performance of employees. Human resource programmes suggested by the employees to improve the effectiveness of workforce diversity has been recommended.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".