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Record W2167856174 · doi:10.3390/ijerph8010185

Energy Solutions, Neo-Liberalism, and Social Diversity in Toronto, Canada

2011· article· en· W2167856174 on OpenAlexaffabout
Cheryl Teelucksingh, Blake Poland

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsPublic Health OntarioUniversity of TorontoToronto Metropolitan University
Fundersnot available
KeywordsEnvironmentalismEnvironmental justiceCapitalismLiberalismMainstreamDominance (genetics)SociologyContext (archaeology)ImmigrationGreen growthPolitical economyPolitical sciencePoliticsLawSustainable development

Abstract

fetched live from OpenAlex

In response to the dominance of green capitalist discourses in Canada's environmental movement, in this paper, we argue that strategies to improve energy policy must also provide mechanisms to address social conflicts and social disparities. Environmental justice is proposed as an alternative to mainstream environmentalism, one that seeks to address systemic social and spatial exclusion encountered by many racialized immigrants in Toronto as a result of neo-liberal and green capitalist municipal policy and that seeks to position marginalized communities as valued contributors to energy solutions. We examine Toronto-based municipal state initiatives aimed at reducing energy use while concurrently stimulating growth (specifically, green economy/green jobs and 'smart growth'). By treating these as instruments of green capitalism, we illustrate the utility of environmental justice applied to energy-related problems and as a means to analyze stakeholders' positions in the context of neo-liberalism and green capitalism, and as opening possibilities for resistance.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.143
GPT teacher head0.379
Teacher spread0.236 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations17
Published2011
Admission routes2
Has abstractyes

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