Equity in Education: The Interconnection between Neighbourhood Characteristics and Public-School Performance
Bibliographic record
Abstract
Public education is highly accessible in high-income countries, with the quality of education guaranteed to a certain extent. Yet international assessment results and research have suggested in-country discrepancies in student performances in these countries, specifically across schools. This study examines how neighbourhood socioeconomic characteristics affect educational outcomes across publicly funded high schools. Using data from the City of Calgary Community Profiles and Government of Alberta Diploma Exam Results, this research analyzes the relationship between three key neighbourhood variables: 1) percentage of immigrants, 2) average education level, 3) median household income, and provincial exam results across public, charter, and Catholic schools in Calgary. As students are designated to specific schools depending on their residency in the city, the student body compositions are made up of students residing in selected neighbourhoods. Through statistical measurement employing descriptive, correlation, and regression analysis, results reveal significant positive associations between neighbourhood education and income level and performances in the diploma exam, with neighbourhood average education level demonstrating the strongest effect. Immigration has a standalone positive influence on performance, though the effect diminished with the inclusion of other neighbourhood variables. Furthermore, the analysis also considers school authority type, where results predict that attending a charter school can lead to higher diploma exam averages, though student composition information in charter schools is more limited than in public schools. The findings highlight the need to consider neighbourhood contexts in policymaking, given the impact on structural sorting of students into different schools and consequently differences in school outcomes. Future research incorporating other community-level factors will be valuable in capturing the complexity in this topic.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".