HRM PRACTICES ACROSS DIFFERENT CULTURES: AN EVIDENCE-BASED STUDY IN CANADA AND JAPAN
Bibliographic record
Abstract
This paper specifically aims to conduct a comprehensive and in-depth analysis of human resource management (HRM) practices in Canada and Japan, with a view to exploring the extent to which culture, social values, history and societal context influence HRM policies and implementation in both countries. This study has used method with a descriptive-qualitative approach was strictly applied to collect empirical data from various relevant secondary references. The data collected covered key aspects of HRM, such as recruitment, selection, training, compensation, Occupational Health and Safety (OHS), and termination of employment. Data analysis was conducted comparatively, focusing on identifying differences, similarities, and common patterns of HRM practices. The results show that HRM in Canada is significantly influenced by social aspirations for inclusiveness, diversity, and equal opportunity, which are reflected in its multidimensional and accommodating employee recruitment, selection, and training systems. On the other hand, HRM in Japan is deeply rooted in a culture of collectivism, group harmony, and organizational loyalty, which is reflected in training programs, compensation schemes, and layoff policies that emphasize collaboration and the sustainability of long-term employment relationships. A deep understanding of the influence of cultural and historical context on the formation of a country's HRM system. This understanding becomes the basis for the development of HRM models and policies that are adaptive and resilient in the face of globalization dynamics, applicable to the context of societies with similar characteristics in other countries.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".