The uphill battle of environmental technologies: Analysis of local discourses on the acceptance and resistance of Green Bin programs
Bibliographic record
Abstract
Many Canadian municipalities have been looking for alternative sustainable waste management solutions since landfill capacity has been decreasing and siting new facilities often results in vehement local opposition. In Ontario, there is no provincial mandate for organic waste diversion targets, where most large-sized municipalities have implemented a Green Bin program while other jurisdictions of varying size still have not. This paper uses discourse analysis to explore predominant and counter discourses that have resulted in Guelph sustaining a Green Bin program, while London has not implemented a Green Bin. Manuscript one explores the interaction of provincial and local municipal discourses in London, Ontario in not adopting a Green Bin program. The findings of this study contribute to understanding the power of discourses in technological and environmental debates to overcome the inertia of the status quo. To examine this further, manuscript two is a comparative case study focused on two municipalities, London and Guelph each with a different approach to the management of organic waste as it relates to Green Bin. This study identified the prominent discourses that represent eco-centric positions, as found in Guelph, are more often discursively juxtaposed against economic conservatism discourses, such as in London. In this study, the discursive positions (eco-centric and conservative) are ingrained within the local municipal discourse and is highly representative of a community coherence on an environmental issue. Overall, the implications of this study find that there is an interface between community coherence and perceived risk of new technology. Such that, in the face of crisis or perceived risk, the community tends to be risk averse, prompting less risky intermediary acceptable risks to be supported.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.020 | 0.023 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".