Bibliographic record
Abstract
Over the past decade, there has been a growing desire to link the fight against climate change more closely with issues of justice. City-based movements for climate justice reveal the overlap between climate vulnerability and other issues that require a profound systemic change towards more socially just forms of urban transformation. However attempts being made to integrate justice into climate planning and action by local governments, these efforts often remain superficial and insufficient. So, how do these two types of actors engage on questions of justice? Our case study identifies the climate equity discourse presented by the City of Montreal, Canada, and certain civil society actors following the publication of the City’s new Climate Plan in the early 2020s, in contrast with certain civil society actors who are strongly mobilised on behalf of the climate. We paid particular attention to outsiders, i.e. actors or communities identifying with the environmental movement and the climate justice discourse but are not involved in formal political decision-making processes. Our results contribute to debates on equity in climate planning by providing data on shortfalls in the City's consideration of justice, and by reporting on civil society's mobilisation on these issues. We conclude that there is no dialogue between the City and outsiders regarding their understandings and representations of climate equity, which poses a risk of developing maladaptation.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.062 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".