The Effect of Vegetation on Student Achievement in the Toronto District School Board
Bibliographic record
Abstract
This dissertation builds on past research that has demonstrated positive and significant correlations between student achievement and vegetation. My research extends The Biophillia Hypothesis, which posits that humans receive psychological benefits from being near vegetation, by hypothesizing that schools with greater amounts of nearby vegetation will have better academic outcomes than schools with lesser amounts of vegetation, controlling for key confounders. Data come from student standardized test scores and demographic, obtained from the Toronto District School Board (TDSB) and from measures of satellite images of vegetation surrounding each school, obtained from the Canadian Urban Environmental Health Research Consortium (CANUE). Initial analyses using Pearson’s correlations and OLS regression show positive relationships between vegetation and achievement similar to those found in previous research. However, this dissertation also engages in a unique attempt to investigate whether vegetation has causal effects on school achievement, exploiting a natural experiment made possible by a large ice storm that hit Toronto in 2013 and destroyed approximately 20% of the city’s tree canopy. Several spatial and descriptive statistics were carried out, including Moran’s I, and long-term vegetation change measurements, to examine whether the ice storm was a significant and random event. Fixed Effects (FE) regression models were run to investigate whether proportions of students who met provincial standards in standardized tests dropped due to vegetation loss after the ice storm. These models test for causal impacts of vegetation by comparing levels of achievement before and after the ice storm while controlling for measured confounders such as school-level SES, as well as unmeasured and time-invariant confounders like school climate and culture. Descriptive statistics and OLS regression reveal mixed associations between vegetation and school achievement, while FE regression models did not yield consistent or significant results in support of the initial hypothesis. Implications of these findings for future research are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".