Running out of time: Public opinions on degrowth in Montréal
Bibliographic record
Abstract
As every nation in the world continues its pursuit for everlasting economic growth, global temperatures are increasing at an alarming rate. For decades, scholars have warned about human impacts on the environment, cautioning us on our exhaustive use of resources and the implications of our seemingly innate need to grow economically. It has been asserted that economic growth is not sustainable, and its contraction is unavoidable due to natural limits, therefore any research on managing and prospering without growth is of great value. Degrowth has been put forth as an alternative paradigm, and as the movement is required to be democratic and collective, public opinions are crucial for its mobilization. Accordingly, public opinions on the matter need to be evaluated in order to assess the potential for implementing degrowth policies. Public opinion studies on degrowth are scarce, especially in North America. This study aims to contribute to the literature by assessing public opinions on degrowth in Montréal, by collecting data on support for 6 degrowth policy proposals (limiting trade distances and volume, creating a moratorium on new infrastructure, taxing resource use, progressive taxation, implementing a basic income and reducing work hours), and support for a societal degrowth transition. I find that the majority of respondents support 4 out of 6 policy proposals, and the majority also support a degrowth transition. This study shows Montréal to be a promising city for experimenting with degrowth politics. Establishing concrete strategies for its implementation that address public concerns could prove degrowth to be a promising avenue for achieving social-environmental sustainability.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".