Infrastructures of Harm, Communities of Knowledge and Environmental Justice
Bibliographic record
Abstract
Marco Armiero on Bodies, Narratives and Commoning in the WasteoceneJust a couple of months before he joined a panel with Divya Gupta in the "Infrastructures of Harm, Communities of Knowledge & Environmental Justice" panel, 1 Marco Armiero was asked in a webinar why he insisted on narratives and storytelling as central in his ideas about the Wasteocene (the homonymous book was released in 2021 as part of the Cambridge Elements series).As an avid Marxist, and Senior Editor of the journal Capitalism Nature Socialism, his answer did not surprise the audience.His words were instead reassuring and filled the online environment with hope and revolutionary energy as they confirmed that a career path asking questions about narratives, nature and justice was not an irrelevant one.He conveyed that seizing the narratives is as important as seizing the means of production.But how to even start with such a Herculean task when narratives, mainly toxic ones, seem so ephemeral and so ubiquitous?Armiero's work is a great inspiration for understanding such narratives as both material and discursive, as fleshed out in his presentation in the "Landscapes of Injustice, Landscapes of Repair" series, organized by the Human Rights Institute at Binghamton and the Norwegian University of Science and Technology.There, he continued dismantling, brick after brick, narratives whose purpose is to naturalize environmental injustice, convince us that the poor choose to be poor and that those living in a polluted community are responsible for its toxicity.Extending
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 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| 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".