Undermining methodological nationalism: Cosmopolitan analysis and visualization of the North American hazardous waste trade
Bibliographic record
Abstract
Drawing on a novel dataset of hazardous waste shipments among Canada, Mexico, and the United States, we seek to enhance dominant modes of understanding transnational trading and regulation at the scale of the nation-state. We argue that these, while valuable, are limited by methodological nationalism. This epistemological position identifies the nation-state as the most relevant unit of analysis in examining “transnational” phenomena. In the case of transboundary waste trading, tracking waste between nation-states has come at the expense of identification and analysis of specific sites within nations that receive hazardous materials or send them abroad, obscuring the ongoing proliferation of waste havens at a subnational level and related environmental justice concerns. Working against methodological nationalism entails an epistemological shift that we pursue in this article through a series of empirical, analytical, and representational practices. We propose three visualization tactics that undermine nation-centered imaginaries: (1) documenting waste havens within the understudied United States through identifying subnational sites importing hazardous waste for processing; (2) establishing connections through flow maps connecting importing and exporting localities transnationally trading specific hazardous wastes; and (3) analyzing the corporate networks dominating the transnational waste trade. We argue these tactics build toward an alternative conception of methodological cosmopolitanism that highlights alternative routes toward environmental justice.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".