Bibliographic record
Abstract
In the struggle to address ecological catastrophe and its deeply entwined social injustices, what can the humanities offer?The second and third events in the Landscapes of Injustice, Landscapes of Repair seminar series took up this question from rhetorical and imaginative perspectives respectively.1 We focus here on rhetorical forms of environmental advocacy presented by Belinda Walzer and Savannah Paige Murray, both of the Rhetoric and Writing Studies Program at Appalachian State University (US), in their seminar, "Climate (In)Justice: A View from the Humanities." 2 With a firm belief in the power of stories to effect change in the world, they argued that it is vital to pay careful attention to the rhetorical strategies we employ to tell these stories.Echoing Marco Armiero's discussion (in the first seminar) of the wasteocene as a valuable term for understanding our current era, Walzer and Murray critiqued mainstream environmental rhetorical frameworks and advocated instead for rhetorics of everyday violence, resistance, and slow violence (Nixon, 2013) to work toward sustainable futures.Rhetoric, the speakers began, does more than simply persuade.Rhetorical frameworks are rooted in temporal, spatial, and ideological contexts.They convey values and build conceptual knowledge based on those values.Attending to rhetorics of climate (in)justice allows us to understand what Walzer and Murray describe as "the deep contextual ecologies of power, discourse, materiality, ethics, and ethos" in a situation.With reference to
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.070 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".