Measuring collective efficacy for inclusion in a global context
Bibliographic record
Abstract
Previous research has identified the importance of teacher attitudes and self-efficacy in supporting inclusive education. This study involved a multi-national exploration of a further dimension of inclusive education, collective efficacy, through the testing of a new tool, the Teacher Efficacy for Inclusive Practice-Collective (TEIP-C) Scale. The study also aimed to investigate whether teacher attitudes, self-efficacy, collective efficacy and intention to teach in inclusive classrooms differ across countries. Participants included 1,523 teachers from Canada, Greece, Italy and Switzerland. Results suggested a two-factor structure for the TEIP-C, Engagement, and Inclusive Pedagogies, with strong internal consistency for the scale. Several differences across countries were identified, with teachers from Italy reporting more positive attitudes towards inclusion and a greater intention to teach in inclusive classrooms. Implications of the study in terms of further strengthening inclusive practice are discussed.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".