Unearthing the common core for reflective teacher training in Antigua and Barbuda, England, and Canada
Bibliographic record
Abstract
Teacher education and training vary across the world, yet research from diverse cultures supports the development of reflective teachers. Claiming that the complex dynamics of the 21st-century classroom can be managed best by reflective teachers. Through the reflective process, teachers are empowered as change agents to modify learning environments to benefit their students. Teachers hold the power to influence student learning. Therefore, it is important to understand the training processes that serve to produce reflective practitioners. In conducting rigorous cross-cultural comparative research, identifying common evaluative indicators underpinned by a theoretical framework is critical. Hence, the principles of the pre-service emerging reflective teacher training (PERTT) model were explored quantitatively and explained qualitatively to specifically focus on teachers’ perceptions of their experiences of the psychosocial interpersonal process outcomes of the teacher-training environment (i.e. reflective practices, instructional scaffolding, guidance and support, modelled behaviours and reinforcement) provided during their teacher training. Such an exploration is critical because the philosophical underpinnings of the PERTT model have not been interrogated outside of Barbados. The findings demonstrate that there is merit to key theoretical principles of the PERTT model based on the lived experiences of the teachers in Antigua and Barbuda, England, and Canada during their teacher training.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".