Yes In My Backyard: Planning For The Development of Contentious Infrastructure
Bibliographic record
Abstract
Waste management is a prevalent and highly contentious issue in modern society. People are often very sensitive about decisions on where and how municipalities choose to dispose of their waste. While it is generally understood that waste disposal facilities such as incinerators and landfills are needed to manage waste that cannot be recycled or composted, there nevertheless seems to be significant opposition in response to any such proposal. This paper will be exploring how communities are currently being engaged in Ontario during the development approval process for incinerators, what motivates communities to actively oppose incinerators, and what can be done to mitigate this opposition. To do this I will be making a case study out of the development approval process for the Durham York Energy Centre (DYEC). The DYEC is a waste-to-energy incinerator in Clarington, Ontario that began operations in 2015. This facility received a significant amount of opposition from the community, which will be explored in detail in this paper. While I focus on incinerator development my goal was to learn the best methods for engaging communities for any undesirable development including landfills and nuclear power plants. \n \nMy research findings suggest that incinerator opposition cannot be mitigated through basic consultation. No amount of consultation will convince people to approve of something they do not want, especially when they feel that it is being forced on them. The goal of community communications plans should not be mitigating opposition but rather should be engaging communities to find optimal strategies for handling communal problems such as waste management or energy. \n \nThis paper will also be looking at alternative waste disposal options for municipal solid waste that cannot be recycled or composted. Having an understanding of the alternatives will allow for a more educated discussion on how municipalities can best manage their waste.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".