Environmental Justice Meets Risk‐Class: The Relational Distribution of Environmental<i>Bads</i>
Bibliographic record
Abstract
Abstract Recent treatments of environmental justice have highlighted the need to move beyond focusing upon inequalities in the distribution of environmental risks to address other aspects of environmental injustice, including unequal participation and recognition. While acknowledging the importance of extending environmental justice to include these other dimensions of justice, this paper argues that more, not less, analytical attention needs to be devoted to the diverse logics of distribution of environmental risks. In light of continuing dilemmas associated with whether environmental inequalities can be just or, alternatively, that environmental inequality and injustice are co‐extensive, this paper proposes to untangle some key connections between environmental inequalities and injustice through a critical confrontation of environmental justice with risk‐class analysis. Focusing on the positional or relational distribution of environmentalbadsas analysed in risk‐class analysis, this paper argues that bringing these two bodies of knowledge together can illuminate how relational inequalities have characteristics that make them particularly illegitimate from a justice perspective, thus making an advance in identifying key connections between environmental inequality and injustice.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".