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Record W1832872573

Analyzing the sustainability of electronic waste management in Toronto, Ontario

2015· dissertation· en· W1832872573 on OpenAlexaboutno aff
Tamara Tukhareli

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2015
Typedissertation
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityElectronic wasteReuseBusinessMaterial flow analysisSustainable managementGovernment (linguistics)Cleaner productionEnvironmental economicsScale (ratio)Environmental planningEngineeringMunicipal solid wasteWaste managementEnvironmental scienceGeographyEconomics
DOInot available

Abstract

fetched live from OpenAlex

Sustainable waste management plays a key role in achieving sustainable urban
\ndevelopment worldwide. Currently, the rates of waste generation are on the increase with
\nelectronic waste comprising a significant portion of the total. This growth in the
\ngeneration of electronic waste has led to the creation of sustainable management
\nprograms in a number of cities, including Toronto in Canada. An examination of the
\nexisting electronic waste management system in Toronto, Ontario has provided many
\ninsights into the structure of the relationships and the flow of the electronic waste within
\nthe area. This thesis analyzes the sustainability of the social networks and material flow
\nnetworks that have developed within the Toronto electronic waste market. The data,
\ncollected from the field observations in the summer, points to a relatively uneven
\ndistribution of partnerships between the large-scale recycling corporations, government
\norganizations, non-profit refurbishers and the informal recyclers. The examination also
\nreveals a prioritization towards large-scale mechanical recycling over refurbishing and reuse
\nof the electronics. The effect of such distribution of material and partnerships on the
\noverall sustainability of the management system is discussed.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.270
Teacher spread0.256 · 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 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
Published2015
Admission routes1
Has abstractyes

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