Towards a Green Economy in Canada
Bibliographic record
Abstract
The paper examines mobilization around the green economy in Canada. Based on a content analysis of media and organizational documents in BC and Ontario, the paper compares the framing strategies of various stakeholder groups, including environmental non-government organizations (ENGOs), social justice groups, labour unions, governments at the federal, provincial and municipal level, in order to determine why the green economies in BC and Ontario progressed differently within the same historical period. The findings point to four main frames used by various stakeholders that advocate for a green economy, including Eco-Bridging, Eco-Equity, Eco-Opportunity, and Eco-Urban Politics of Sustainability. The paper discusses these overlapping, but often competing frames as well as the opportunities for uniting diverse frames in order to increase the impact on policy outcomes in both regions. The paper concludes by pointing to the need for a new master frame to link environmental and social justice movements, to reflect the social, economic and environmental dimensions of an equitable and robust green economy. The paper argues that a Social Determinants of Health framework is well-suited for linking these dimensions because it highlights the root causes of inequities, including how these manifest more acutely in some populations than others, and underscores the link between environmental outcomes and social well-being.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".