The Sustainable City, Myth or Possibility, as seen through the eyes of the citizens and the lens of wellbeing
Bibliographic record
Abstract
This paper addresses the question of barriers and enablers to community-based climate action as seen through the eyes of residents in the community. The sample community is Stratford, Ontario. 32 persons in Stratford were interviewed in individual, private conversations and were asked to answer 10 questions. The responses of the participants revealed concerns analogous to those found in various wellbeing frameworks. To provide background for the feedback from community interviewees the paper explores the context of wellbeing, following its historical and theoretical roots to its applications today. The wellbeing story begins with a critique of GDP as a single metric to reflect the condition of society and argues for the application of a wellbeing lens as a tool to better tell us how society is doing overall, including environmental, ecological and social justice issues. The author provides a brief history of the evolution of the concept of wellbeing including the origins and application of hedonic and eudaimonic conceptions of wellbeing as related to sustainability. The position taken is that the eudaimonic aspect is more suited to addressing long term environmental and ecological concerns. Following on from the declaration of the 2015 Paris Climate Accord and the creation of the UN's Sustainable Development Goals, the paper charts the development and application of wellbeing frameworks and concepts in cities, communities and countries around the world, including the Wellbeing Economy Alliance founded in 2018. Wellbeing practices in communities in Canada are surveyed and Canada's wellbeing framework created in 2021, Measuring What Matters: Toward a Quality of Life Strategy for Canada, is introduced. With a focus on wellbeing in cities, the governance structures and challenges in tackling climate action at the municipal level are examined. The application of wellbeing metrics is suggested to give municipalities a picture of how their society and their environs are doing overall, beyond emissions reporting and the silo of GDP-driven economic reporting. Transition practices for cities provide references and tools for the application of wellbeing metrics. The embedded and particular relationship which cities have to Indigenous Peoples is presented with examples from cities in Canada in the light of Indigenous knowledges and practices in the care of people and planet. Cities are reminded of their obligations under the Calls to Action of the Truth and Reconciliation Commission and the requirements for care of lands and waters and cultural traditions under UNDRIP. Methods and summary data are presented in section 3, followed by findings from the 10 questions. The responses are analysed applying wellbeing metrics.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.101 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".