Energy justice in education sector: The impact of student demographics on elementary and secondary school energy consumption
Bibliographic record
Abstract
Reducing school energy costs has become an important issue, while the energy saving should consider different school systems and student backgrounds. This study investigated the impact of student demographics on energy consumption in elementary and secondary schools and explores the difference of energy consumption in different types and levels of school systems. Data were collected from 3672 schools (including 3108 elementary and 564 secondary schools, respectively) in Ontario, Canada. The number of students whose first language is not English, the number of students who receive special education services, the number of school-aged children who live in low-income households, and student learning ability are all inversely proportional to energy consumption; student learning ability has the largest negative impact. The partial correlation between student enrollment and energy consumption has a trend of gradually increasing as the grade levels increase in Catholic elementary schools, Catholic secondary schools, and public secondary schools; however, the correlation shows a gradually decreasing trend with the increase in grade levels in public elementary schools. This study is helpful for policy-makers to clarify the energy implications of various student backgrounds and the energy consumption difference in different types and levels of school systems to facilitate their formulation of effective policies.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".