The Type of Culture at a High Performance Schools and Low Performance School in the State of Kedah
Bibliographic record
Abstract
This research aims to identify the type of culture at a High Performance School (HPS) and Low Performance School (LPS) in the state of Kedah. The research instrument used to measure the type of organizational culture was adapted from Organizational Culture Assessment Instrument (Cameron & Quinn, 2006) based on Competing Values Framework Quinn and Rohrbaugh (1983) Model. Two HPSs and two LPSs representing 129 teachers were chosen as research samples. Research findings showed that the current dominant culture in both HPS and LPS is Hierarchy (M=36.76, M=30.63 & SD = 8.23, SD=7.83) consecutively. Both types of schools practice Market culture and the average scores for both types of schools are the same whereby HPS (M=25.7, SD=8.56) and LPS (M=25.94, SD=8.32). However, HPS practices less Clan culture (M=21.15, SD=8.74) compared to LPS (M=24.18, SD=6.97). The Clan culture is more prominent in LPS compared to HPS. Both types of schools practice Adhocracy less which are (M=17.75, SD=6.94) in HPS and (M=19.81, SD=6.78) in LPS respectively. The findings showed that teachers in both types of schools would like to have the Clan culture practiced in their schools with the same average scores, M=34.89, SD=8.22. HPS prefers the Market culture (M=26.01, SD=6.32) compared to LPS Market culture (M=23.01, SD=.22). However they accepted Adhocracy less in HPS (M=20.02, SD=8.31) and LPS (M=21.58, SD=7.89). Nevertheless, Hierarchy culture in HPS (M=19.32, SD=6.45) and in LPS (M=21.05, SD=6.68) are widely practiced. From the comparisons made, it can be concluded that both types of schools widely practice the Hierarchy and Market cultures in schools. The recommendations for further studies were also suggested.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".