Models of justice evoked in published scientific studies of plastic pollution
Bibliographic record
Abstract
An exponentially growing body of international research engages with plastic pollution using different ideas on the right ways to frame, research, and intervene in the problem. The premise of this study is that all scientists work with understandings of what is right and wrong and why that is (models of justice) in their research, even when it is not explicitly stated, reflected upon, or a conscious part of the discussion. We surveyed 755 published articles on marine debris and plastic chemical additives and found that all evoked at least one model of justice, and often more. The most routinely used models included: developmental justice, distributive justice, and procedural justice. More rarely, we found appeals to environment-first justice and Indigenous sovereignty. While occasionally these multiple models worked synergistically, more often they conflicted. Our findings ground a call for fellow researchers to use a more intentional and systematic approach to evoking models of justice in our work. Our goal is to offer descriptions and insights about models of justice that are already being deployed to increase the sophistication of the ethical and normative orientations of our research and our fields, both in plastic pollution sciences and beyond.
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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 | 0.142 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.010 | 0.040 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.006 |
| 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".