Change Management Strategies in Policies and Reforms and Administrative Functions Competence in Delta State Colleges of Education
Bibliographic record
Abstract
This research change management strategies and administrative functions competence, used the expo-facto researchdesign of the survey method. The population of the study was made up of the 865 lecturers in the Delta StateColleges of Education in the 2015/216 academic session. The study used the stratified random sampling technique tosample 120 lecturers. The questionnaire tagged “Change Management Strategies and Administrative FunctionCompetence” (CAMSAFC) was the main instrument used. It was validated by expert judgement and tested forreliability using the split-half method and Pearson Moment Correlation co-efficient statistics to obtain a reliabilityindex of 0.75, from 20 respondents of the College of Education, Ekiadalor, Edo State. The results/data from the fourresearch questions were presented and analyzed on a table using the mean scores and standard deviation with theacceptance mean rating at 2.50 and above. The only hypothesis raised was tested using the t-test statistics forsignificant difference in the perception of respondents at 0.05 level of significance. The findings revealed that staffand students personnel services, curriculum and instruction, school community relations, school finance, school plant,are the main administrative functions that experience change frequently and that change management strategiescommonly used includes; articulation of problems to be solved with change, collaborative approach in plan andimplementing change, among others. The findings also revealed that there is no significant difference in theperception of respondents when experience is considered.
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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 | 0.005 | 0.011 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".