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Record W4384695103 · doi:10.22215/etd/2023-15620

The Waste Crisis: Speculative Architecture for Waste Reduction and Public Awareness in Downtown Toronto

2023· dissertation· en· W4384695103 on OpenAlexaboutno aff
Hye Yoon Ahn

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsMindsetDowntownReuseCrisis managementCleaner productionBusinessEnvironmental planningArchitectureEngineeringWaste managementMunicipal solid wasteEconomicsEnvironmental scienceGeographyManagement

Abstract

fetched live from OpenAlex

The waste crisis is an escalating global environmental issue resulting from colonial and extractivist modes of thinking and living. This crisis has far-reaching consequences, including environmental pollution, human and nonhuman displacement, and climate change. In addition, the crisis has economic and spatial implications due to high waste management costs and the proliferation of landfills that keep waste out of sight while damaging ecosystems and communities. The challenge of this crisis demands to address the fundamental causes of the waste crisis that, beyond managing waste, requires a change of mindset. Through a compilation of data gathering and analysis of current waste production and management strategies in Toronto, this thesis proposes a speculative long-term transformation strategy that aims to recover, repair, and reuse buildings, objects, and materials during the lifetime of the Commissioner transfer station while also educating and raising social awareness to influence collective behavior even after its death.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.868

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.006
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.018
GPT teacher head0.311
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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