School Climate and Pupils' Progress
Bibliographic record
Abstract
The school climate shapes the day-to-day learning experiences of the pupils, which influences their motivation,engagement, and academic achievement. This study sought to determine the parents’ perception of school climate, the level ofpupils’ progress, and the significant relationship between the school climate and pupils’ progress among the six (6) schools ofTalisayan District, Division of Misamis Oriental, during the Second Quarter of the School Year 2023-2024. There was a total ofone hundred twelve (112) Grade 2 parents’ respondents through stratified random sampling method. This study utilized anadapted and modified survey questionnaire developed by Karen Parker Thompson, coordinator of family involvement andcommunity resources for the Alexandria City Public Schools. It employed the mean, standard deviation, and Pearson ProductMoment Correlation Coefficient (r) to ascertain the significant relationship between parents’ perception of school climate andpupils’ progress. The study found that the respondents have a Very High perception of the school climate, specifically the caringlearning environment. Almost half of the respondents have an Outstanding rating. A significant strong positive correlation existsbetween the school climate and pupils’ progress, thus rejecting the null hypothesis and that the school climate fosters a caringlearning environment. It is therefore recommended to create a school environment that is conducive, safe, and supportive topupils’ learning and academic growth. Meanwhile, it is believed that parents agree with enhancing the effectiveness of problem-solving strategies in school
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".