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Record W7132985461

Exploring Policy-mandated Building Design Requirements as an Intervention for Waste Diversion in Multi-residential Buildings: A Case Study of the City of Toronto

2022· dissertation· W7132985461 on OpenAlexaboutno aff
Emily Leung

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
Fundersnot available
KeywordsIntervention (counseling)Building designDesign methodsSystems design
DOInot available

Abstract

fetched live from OpenAlex

This research seeks to review a City of Toronto policy which specifies which waste sorting systems must be implemented into multi-residential building designs. The study draws on socio-technical systems and technological fix as theoretical frameworks to gauge how effective the required waste collection systems actually are and how well the users interact with the systems. Semi-structured interviews were conducted with nine property managers with different chute systems to gauge effectiveness. To expand the policy context, interviews with policy makers of design standards of chute designs in five municipalities were also conducted to explore the rationale behind their requirements. Findings show that some municipalities are starting to move away from technological waste chute systems. The technological chute systems were found to be regularly malfunctioning and needing maintenance and were also used incorrectly by residents—all of which led to more contamination. Efforts to increase waste diversion at MRBs through design will need the expertise of occupants and would benefit from more operational standards to understand diversion technology design flaws.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.187
GPT teacher head0.421
Teacher spread0.234 · 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 designQualitative
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

Citations0
Published2022
Admission routes1
Has abstractyes

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