Plastic Earth: Environmental Justice, Hope in the Global Plastics Treaty, and Reproductive Eco-Anxiety in a Time of Environmental Harm
Bibliographic record
Abstract
This dissertation takes a multi-scalar approach to exploring the environmental and human health hazards of plastics. The first and second studies explore global issues of plastics through environmental justice (EJ) and the United Nations plastics treaty negotiations respectively. The first study narrates the plastics “life cycle” through connecting existing literature and case studies of distributive, recognitional, and procedural injustices at each cycle stage. In addition to peer reviewed scholarship and grey literature, this chapter operationalizes the voices of those most impacted as data commensurate to formal studies. The second study explores how the plastics industry and the global EJ coalition communicate opposing stances on “production reduction” in the plastics treaty. I share my research from attendance at the 4th UN Intergovernmental Negotiating Committee session (INC-4) in Ottawa, CA. The third study turns to the personal scale. Through autoethnography, I situate my personal experience with pregnancy, birth, and new motherhood in relationship with plastics through a critical, intersectional feminist lens. In this chapter, I explore the affect and anxiety around the paradox of questionable health and environmental repercussions of ubiquitous plastic, and the simultaneous feelings of ease and relief these plastics produce. Taken together, these chapters create a multidimensional analysis of plastics. I document the systemic, global nature of the environmental injustices of plastics, imbue readers with hope based on the EJ work of dedicated individuals working on the global plastics treaty, and provide a blueprint for those wrestling with the emotional tensions of a personal relationship with plastic materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".