Motivation, engagement, and social climate: An international study of boarding schools.
Bibliographic record
Abstract
Most educational climate research is conducted among (day school) students who spend the bulk of their young lives outside of school, potentially limiting the amount of climate variance that can be captured. Boarding school students, on the other hand, spend much of their lives at school and thus offer a potentially unique perspective on educational climate. The present study comprises an international sample (United States, Canada, United Kingdom, and Australia) of 3,274 high school students from 121 boarding houses nested under 21 schools. The study is a multilevel one that explores variance in boarding house motivation, engagement, and social climate at multiple levels of a nested educational structure: student, boarding house, and school. Once sociodemographic, prior achievement, personality, and boarding characteristics were entered as covariates, findings showed that on all climate measures there is greater variation from student-to-student than there is from house-to-house or school-to-school. Interestingly, house climate ratings tended to vary more from school-to-school, than from house-to-house. Of the covariates, gender, personality, and time spent in boarding school predicted numerous motivation, engagement, and social climate factors. Overall, findings suggest that boarding house climate is very much in the eye of the individual boarder. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".