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Record W3089157062 · doi:10.11575/prism/38232

Waste Collection Technologies, Informal Waste Pickers, and Urban Exclusion: A Case Study of Calgary

2020· dissertation· en· W3089157062 on OpenAlexaboutno aff
Dare Moses Adeyemi

Bibliographic record

VenueOpen MIND · 2020
Typedissertation
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
Fundersnot available
KeywordsHousehold wasteWaste collectionUrban wasteEnvironmental planningBusinessGeographyEngineeringWaste managementMunicipal solid waste

Abstract

fetched live from OpenAlex

Waste management engineers and administrators have conceived of technological efficiency and optimization as the “modern” way to sustainable waste collection and management. This instrumental ideology of technology offers a progressive chant for modern waste collection technologies and a less enthusiastic one for the tools and techniques of informal waste pickers. Few efforts have gone into conceptualizing the social context and implication of waste collection technologies. In this thesis, I used a qualitative case study to explore the impact of residential waste collection technologies on the exclusion of informal waste pickers in Calgary. I draw on Andrew Feenberg's critical theory of technology to situate waste collection technologies within social, economic, and political contexts in Calgary. I argue that the social relations of ownership and control over waste collection technologies in Calgary illustrate complex and contested values, norms, and privileges, which create an unequal social, material, and technical relationship contributing to the exclusion of pickers and the exploitation of labor and waste. Calgary’s new curbside program protects the social norms of private asset ownership and consumerism, as well as the interest of private homeowners and some bureaucratic and large capitalist individuals in Calgary. A local third-sector organization, Calgary Can, has resisted these acts through its hook program; local bottle pickers have also resisted them through their collection activity and technologies. These realities push back against the colloquial understanding of modern waste collection technologies as value-free, a conception that dominates academic research and city policies and programs in waste management.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.023
GPT teacher head0.275
Teacher spread0.252 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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Same venueOpen MINDSame topicMunicipal Solid Waste ManagementFrench-language works237,207