Examining the Relationship between Teachers’ Individual Innovativeness and Professionalism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of this study was to examine the relationship between teachers’ individual innovativeness and their teacher professionalism. The participants were 567 teachers working in elementary, middle and high schools located across Istanbul. The data were gathered through the "Individual Innovativeness Scale" and the "Teacher Professionalism Scale". In data analysis, arithmetic means and Pearson Product-Moment Correlation Analysis were used. The results of the study showed that the teachers’ characteristics of individual innovativeness fell into the group of early majority. The teachers’ innovativeness was found to be at the highest level in the dimension of openness to experience. There was a weak positive relationship between the individual innovativeness characteristics of openness to experience and opinion-leading, and teacher professionalism, which was significant.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it