Bibliographic record
Abstract
The aim of this study is to explore the characteristics of innovative personality among teachers in Malaysia. Samples of the research were randomly selected among secondary school teachers in three districts in Malaysia. Research instrument was self-developed by the researchers based on interviews carried out with some resource persons who are both experts and authoritative in their fields, as well as through literature review. A pilot study was carried out among 30 respondents. Cronbach’s Alpha value for the whole instrument is .952, indicating that it is reliable and suitable for actual data collection. A total of 484 sets of questionnaires were completed and gathered to form the data for this research. The data were then analysed using an advanced statistical method called Principal Component Analysis (PCA). Findings of the research concluded three constructs, namely, Leadership, Openness and Braveness. The constructs were labelled based on groups of items which were formed as a result of the PCA analysis. Meanwhile, Confirmatory Factor Analysis (CFA) was used to validate each dimension and to analyse the coherence of data based on model hypothesis. The findings of CFA indicated the goodness-of-fit values of the revised model, as follows: CMIN/DF=2.56; CFI=.935; and RMSEA=.057; with each figure above the threshold value.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".