Bibliographic record
Abstract
The aim of this research study was to investigate the Leadership Practices, School Environment and Academic Achievement at Secondary Schools in Nasirabad division, Balochistan province. The study was also aimed at exploring the effect of head teachers` Leadership Practices on Academic Achievement and School Environment at Secondary Schools. The related literature was reviewed which dealt with different aspects of leadership practices along with school environment which suits to run the school matters better of learning and teaching environment which eventually lead to improve academic achievement of students. Methodology: The descriptive survey design with quantitative approach was used to conduct this study. A descriptive survey approach is more appropriate for collecting a huge data from the large number of sample at one point in time (Babbie, 2007; Cresswell, 2008). Likert Scale Questionnaire 0.5 was used to collect the response from participants. Data Analysis: Statistical analysis technique was used to analyze the data via SPSS statistical software Findings: The findings of the regression analysis revealed that there is a positive impact of leadership practices on school environment. It was also revealed by the statistical analysis that the better leadership practices can bring about a prominent change in students` academic achievement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.904 | 0.885 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".