Premodern Ecologies in the Modern Literary Imagination. Edited by Vin Nardizzi and Tiffany Jo Werth
Bibliographic record
Abstract
What does premodern literature have to offer twenty-first-century conversations about the environment? Since 1610 was recently (and controversially) proposed as a possible start-year for the Anthropocene, this question has never been more pressing. And this collection, edited by Vin Nardizzi and Tiffany Jo Werth, attempts to answer it through a series of case studies in “situated knowledge” (5) or rather “conversations from different hubs in time and space” (10), which trace a few of the ways in which the “freighted and imperfect” (9) category of the premodern has sustained some strands of environmental thinking throughout history. These conversations, initiated at a 2015 symposium inspired by Ursula K. Heise’s theory of “eco-cosmopolitanism,” approach premodern literature from a dizzying array of historical and geographical perspectives. Contributors hail from New Zealand, Canada, Australia, England, South Africa, and the United States and tackle topics ranging from medieval manuscripts to seventeenth-century astrolabes, from Victorian neogothic architecture to local California viticulture. Shakespeare, a key figure for most premodern ecocriticism, is refreshingly sidelined for much of the collection.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".