Afterword: Ecofeminism through Literary Activism, Hybridity, Connections, and Caring
Bibliographic record
Abstract
Ecofeminism can never only be an academic discourse. Its roots are in lived experiences, and harsh ones at that-in struggles for survival, and care, for both human and nonhuman. This chapter suggests that academic writing, and specifically literary theory can be a form of activism, and ought to be if it is ecofeminist. The chapter highlights the ethic of care that several of the authors find important in their ecofeminist criticism. The chapter suggests that, as Karen Warren claimed, caring relationships can be modelled on traditionallyt—hough not essentially—female interactions and roles. The chapter draws important connections that so many pieces in this collection point to, either implicitly or explicitly. In Canada there have been numerous water crises within First Nations communities. In the United States, a water crisis in Flint, Michigan, beginning in 2014, saw city water contaminated with bacteria, lead, and disinfectants.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".