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Record W4376127436 · doi:10.1016/j.heliyon.2023.e16191

Energy justice in education sector: The impact of student demographics on elementary and secondary school energy consumption

2023· article· en· W4376127436 on OpenAlexaboutno aff
Zefeng Huang, Zhonghua Gou, Senhong Cai

Bibliographic record

VenueHeliyon · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsEnergy consumptionDemographicsConsumption (sociology)Mathematics educationEnergy (signal processing)PsychologyMedical educationDemographySociologyMedicineEngineeringMathematicsStatisticsSocial science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.012
GPT teacher head0.298
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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