Bibliographic record
Abstract
Abstract Much ecocriticism scholarship published in 2021 sought to tackle the question of environmental emergency. The work considered in this essay covers a considerable range of topics, temporalities, and spaces, ranging from British histories of industrial extraction to Canada’s polluted waterways in the present, and draws upon an array of disciplinary contexts, including work from literary theory, media studies, the environmental humanities, waste and discard studies, Black studies, feminist theory, and Indigenous studies. I examine works that suggest new ways of understanding our current ‘crisis’ through a reframing of timescales and an emphasis on infrastructures; works that think explicitly about extraction and extractivism by considering what critical and cultural theory can offer to help better conceptualize both the climate crisis and the structures of racial capitalism on which it is grounded; and works that employ and develop experimental, hybrid forms of creative-theoretical research methods, operating outside the bounds of what we might traditionally understand as ecocriticism. I conclude by suggesting that these experimental modes of theory exemplify how ecocriticism and the environmental humanities are increasingly turning to new forms to imagine possible routes out of contemporary intersecting crises. The essay is divided into four key sections: 1. Introduction: Code Red for Humanity; 2. The Colonial Logics and Infrastructures of Environmental Crisis; 3. Theorizing Extraction and Extractivism; 4. Finding New Forms.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".