Multispecies justice: Climate‐just futures with, for and beyond humans
Bibliographic record
Abstract
Abstract In 2019, the climate emergency entered mainstream debates. The normative frame of climate justice as conceived in academia, policy arenas, and grassroots action, although imperative and growing in popularity across climate movements, is no longer adequate to address this emergency. This is for two reasons: first, as a framing for the problem, current notions of climate justice are insufficient to overcome the persistent silencing of voices belonging to multiple “others”; and second, they do not question, and thus implicitly condone, human exceptionalism and the violence it enacts, historically and in this era of the Anthropocene. Therefore, we advocate for the concept of multispecies justice to enrich climate justice in order to more effectively confront the climate crisis. The advantage of reconceptualizing climate justice in this way is that it becomes more inclusive; it acknowledges the differential histories and practices of social, environmental, and ecological harm, while opening just pathways into uncertain futures. A multispecies justice lens expands climate justice by decentering the human and by recognizing the everyday interactions that bind individuals and societies to networks of close and distant others, including other people and more‐than‐human beings. Such a relational lens provides a vital scientific, practical, material, and ethical road map for navigating the complex responsibilities and politics in the climate crisis. Most importantly, it delineates what genuine flourishing could mean, what systemic transformations may involve (and with whom), how to live with inevitable and possibly intolerable losses, and how to prefigure and enact alternative and just futures. This article is categorized under: Climate, Nature, and Ethics > Climate Change and Global Justice
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.065 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".