Adoption of Human Resources Management Policies for Practices: Harvard Model versus Religious Model
Bibliographic record
Abstract
The paper set out to explore two different models of Human Resources Management as a policy for practice that will be adequate for adoption by any organisation. The Harvard and Religious models were the two models critically examined vis – a – vis their implications on the practice of Human Resources Management (HRM). It was revealed that Harvard model of HRM is a content model as it is contingent on specific core issues (work system, reward system, employees’ influence and flow of people) in human resources management while Religious model of HRM is a process model as it is based on identification of relationship among components units (management and employees) Harvard model of HRM as a policy is embedded in the organisation through congruence, commitment, cost effectiveness and competence and Religious model of HRM is anchored on value based ideology through morality, honesty, sincerity, fairness and integrity. The two models are practicable but Harvard model of HRM has no exception to a particular party in business organisation while Religious model of HRM is averse to development of trade union in organisation. Therefore, the adoption of the two models will make world of work conducive, however, Harvard model of HRM aligned more with the nature and belief of business. However, combination of the two models to give a contingency – hybrid model will make the workplace to be better than adopting one of the models.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".