School Composition and Contextual Effects on Student Outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background Findings from several international studies have shown that there is a significant relationship between literacy skills and socioeconomic status (SES). Research has also shown that schools differ considerably in their student outcomes, even after taking account of students’ ability and family background. The context or learning environment of a school or classroom is an important determinant of the rate at which children learn. The literature has traditionally used school composition, particularly the mean SES of the school, as a proxy for context. Focus of Study This study examines the relationships among school composition, several aspects of school and classroom context, and students’ literacy skills in science. Population The study uses data from the 2006 PISA (Programme for International Student Assessment) for 57 countries. PISA assesses the knowledge and life skills of 15-year-old youth as they approach the end of their compulsory period of schooling. Research Design Secondary analyses of the data describe the socioeconomic gradient (the relationship between a student outcome and SES) and the school profile (the relationship between average school performance and school composition) using data for the United States as an example. The analyses demonstrate two important relationships between school composition and the socioeconomic gradient and distinguish between two types of segregation, referred to as horizontal and vertical segregation. The analyses discern the extent to which school composition and classroom and school context separately and jointly account for variation in student achievement. Findings The results show that school composition is correlated with several aspects of school and classroom context and that these factors are associated with students’ science literacy. Literacy performance is associated with the extent to which school systems are segregated “horizontally,” based on the distribution among schools of students from differing SES backgrounds, and “vertically,” due mainly to mechanisms that select students into different types of schools. Conclusions An understanding of socioeconomic gradients and school profiles for a school system is critical to discerning whether reform efforts should be directed mainly at improving the performance of particular schools or at striving to alter policies and practices within all schools. Both horizontal and vertical segregation are associated with lower student outcomes; therefore, we require a better understanding of the mechanisms through which students are allocated to schools. When the correlation of school composition with a particular contextual variable is strong, it calls for policies aimed at increasing inclusion or differentially allocating school and classroom resources among schools serving students of differing status.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it