Identifying profiles of school climate in high schools.
Bibliographic record
Abstract
This cross-sectional study analyzed data from 364,143 students in 492 high schools who completed the Georgia School Climate Survey during the 2017-2018 school year. Through latent profile analysis, we identified that student perceptions of school climate could be classified into three distinct profiles, including positive, moderate, and negative climate. Using multinomial logistic regression, we then identified school and student characteristics that predicted student classification in the student profiles using the total sample and subsamples by race/ethnicity. Among the key results, we found that most of the school characteristics (e.g., percent of students receiving free or reduced lunch, schools with higher percentages of minoritized students) predicting classification in the negative and positive school climate profiles were different for White students compared to minoritized students. For example, Black students in primarily non-White schools were more likely to view school climate positively, whereas the opposite was the case for White students. We also found that Black and Other (e.g., multiracial) students were more likely to be classified in the negative school climate profile and less likely to be classified in the positive school climate profile compared to White students. In contrast, Latino/a/e students were more likely to be classified in the positive school climate profile and less likely to be classified in the negative school climate profile. Implications for research and practice are discussed. (PsycInfo Database Record (c) 2024 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".