Reduce, Reuse, Recycle and Other Plastic Rhetoric: Examining the Influence of Environmental Rhetoric on Plastic Governance in Canada
Bibliographic record
Abstract
As the plastic crisis has emerged as a prominent environmental issue, among others like climate change and biodiversity loss, it has become increasingly important to examine current models of plastic governance. Studying plastic governance provides opportunities to better understand, challenge and hopefully improve upon the landscape of governance within the plastic crisis. For the purposes of this paper, plastic governance is defined as the management and regulation of plastic throughout its life cycle, from production to disposal. The plastic crisis has complex socio-political and ecological dimensions that shape both plastic's problems and potential solutions. Environmental rhetoric surrounding plastic and plastic governance fosters, as well as exemplifies, the dynamic human and ecological dimensions at play in the plastic crisis. For example, the prominence of rhetoric like "Reduce, Reuse and Recycle" and recycling generally demonstrate the creation and reinforcement of waste management and consumer responsibility as the main problems and solutions within plastic governance. Focusing on Canada and the recent announcement of a national ban on single-use plastic, I explore the impacts of environmental rhetoric on the existing and developing governance landscape for plastic. By examining the use of or appeal to environmental rhetoric among state, corporate and civil actors in Canada, I apply concepts from the social science theories of critical political ecology and constructivism in order to gain a deeper understanding of the political and ecological dimensions of the plastic crisis and governance. In particular, I examine the ways in which the coproduction of science and politics informs the characterization of the plastic crisis itself as well as any potential solutions. By studying how rhetoric shapes the problems and solutions we see and emphasize, I will identify and explore norms and gaps within governance throughout plastic’s life cycle. As well, I consider rhetoric surrounding the actors within the plastic crisis and examine how identity and perception play a role in plastic governance. This research allows for deeper critique of plastic policies or initiatives in order to provide potential recommendations for advancing environmental sustainability in Canada.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".