Teacher-Candidates' Perceptions of School Climate: A Mixed Methods Investigation
Bibliographic record
Abstract
Preservice teacher-candidates are assigned to a number of different schools for their practicum experiences and as a result are immersed in a variety of school cultures and their respective climates. Interestingly, though matters related to school climate and the hidden curriculum are discussed in the literature, there is a lack of comprehensive research around preservice teachers’ expectations and observations during their formal teacher education program. Given that beginning teachers’ experiences are intensely impacted by their observations and experiences throughout their teacher training, the purpose of the study was to investigate teacher candidates’ beliefs about the climate of schools at the beginning and near completion of their teacher education program. More specifically, this study employed a mixed methods research design to determine how beliefs about the hidden curriculum of schools compared to teacher candidates’ impressions as they gained practice-teaching experience in various schools. The results may induce preservice education faculty to evaluate the underlying pedagogical causes that profoundly illuminate and engagingly implicate the tensions within teacher candidates’ expectations of school climate and their observed realities.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".