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Record W7055280789

Climate justice: building socio-economic equity for climate action

2024· article· en· W7055280789 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2024
Typearticle
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsClimate justiceTaxpayerCarbon taxClimate changeEquity (law)LaggingFossil fuelCarbon footprintPer capitaSubsidy
DOInot available

Abstract

fetched live from OpenAlex

This workshop will provide a networking and publications opportunity for CANSEE participants who are interested in mobilizing their own research and practical ideas related to climate justice (both within Canada and globally) for policy, activism, and public education.\n\nParticipants will be invited to share their own experiences and research and to coordinate actions to accelerate an equitable energy transition in Canada. These may include contributions to a special issue of Capitalism Nature Socialism (CNS) or another journal; a planned series of newspaper op-ed articles and blogs; submissions to Canadian Dimension, The Conversation, the Narwhal, Rabble.ca, This Magazine, Ricochet, The Walrus, Indigenous Watchdog, The Tyee, and other publications; planned protests in collaboration with environmental NGOs and community-based organizations; organized actions in relation to current events; and/or other networking and action ideas in accordance with participants’ interests.\n\nCanadians’ carbon footprint per capita is among the highest in the world, lagging mainly small Middle Eastern oil-producing nations. This reflects our high energy use for both heating and cooling, transportation, and high-income lifestyles, as well as the structure of the Canadian economy. Emissions from oil and gas extraction (including the tarsands), and from agriculture (including natural gas-based fertilizer use) are still increasing, while emissions from heating and cooling buildings, transportation, and most other sectors, have gradually begun to decline. Estimates of taxpayer subsidies to the Canadian fossil fuel industry range from $4.5 bn to $18 bn per year – more than any other G20 country. New tax credits to high-emitters for ‘carbon capture and storage’ are likely to add billions to this total. Canadian peatlands, which store twice as much carbon as all the world’s forests, are beginning to release carbon due to permafrost thawing, water loss, and fires, which could result in a worsening cycle of new emissions from Canada over the coming decades, comparable in quantity to those of Europe today. We know that climate chaos hurts the vulnerable first and hardest. Equity and socialtrust are important determinants of all countries’ ability to efficiently and rapidly implement progressive emissions-reduction and energy-transition policies.\n\nI am teaching a Climate Justice field course at York this summer which will include opportunities for graduate and undergraduate students to research and write about climate justice case studies; they would all be potential participants and contributors to this workshop. I am an editorial group member for CNS and the coordinator of Women & Environments International magazine, which could facilitate publication opportunities. This workshop will respond to the interests and priorities of all participants as we share strategies for how Canada can urgently reshape its global climate justice priorities.

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)0.019
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.172
Threshold uncertainty score0.342

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0220.035
Scholarly communication0.0260.014
Open science0.0030.030
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0250.002

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 head0.025
GPT teacher head0.220
Teacher spread0.196 · 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 designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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