Human Resource Management Practices in Nigeria
Bibliographic record
Abstract
The globalization of business is having a significant impact on human resource management practices; and it is has now become more imperative than ever for business organizations to engage in human resource management practices on an international standard. While the management of people is mostly associated with HRM, the definition, parameter and context are contested by different writers. Some authors such as Kane (1996) argued that HRM is in its infancy, while other authors such as Welbourne and Andrews (1996) dispute it. However, other writers have attempted to differentiate between personnel management and HRM (Sisson, 1990), by emphasizing on the strategic approach to managing people. Other writers such as Legge (1995) have focused on the soft and hard approach to managing human resources. All these distinctions have contributed to the fundamental differences in understanding and defining human resource management practices, and therefore, HRM should not be incorporated within a single model, but rather adequate emphasis should be on understanding human resource management issues, which will assists practitioners, authors, mangers and organizations in developing and implementing HRM policies and practices that will be productive and that can make businesses to gain and sustain a competitive advantage. This is paper is aimed at exploring HRM practices in Nigeria.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".