HRM Practices in Public and Private Universities of Pakistan: A Comparative Study
Bibliographic record
Abstract
The purpose of this study was to compare the HRM practices of public and private universities in Punjab province of Pakistan. The data for the study was collected through a questionnaire comprising 30 items mainly related to job definition, training and development, compensation, team work, employee’s participation and performance appraisal. The instrument was validated through pilot testing. The internal reliability of the instrument was found to be 0.85. The sample was comprised of 60 executives (directors/heads of departments) selected randomly from six universities. The collected data was analyzed by applying descriptive and inferential statistical techniques such as means and independent sample t-test. The results showed that there was a significant difference in HRM practices according to executives of public and private universities. HRM practices in the areas of job definition, training and development, compensation, team work and employees participation were better in the public universities than private universities. However, performance appraisal practices were found better in the private universities than public sector universities. At the end recommendations were made for the HRM executives of private and public universities to improve their HRM practices in favor of their employees.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".