Back on the Farm: The Trade-offs in Ecocritical Lives
Bibliographic record
Abstract
In this reflective dialogue, the authors explore how the decisions they are making about where and how to live, teach, and write are reflected in the concerns of the field of ecocriticism, as well as in the agricultural fields of their rural home places. The apparent tension between lived ecocritical practice and productive ecocritical scholarship suggests that individuals must make a difficult trade-off, in which they give up one aspect of ecocriticism in order to gain the other. But the authors argue that by understanding individual trade-offs in more nuanced ways—as investments of energy within complex ecological and social relationships—it is possible to reflect on the assumptions that frame our choices and to envision new choices. Ecocriticism could offer a method for optimizing the systems people use to produce and share ideas. For example, ecocritical scholarship could take new forms, of which the authors’ conversation is one example. Since ecocriticism must strive for diversity as well as inclusivity in order to be relevant, the authors find that marginal voices will continue to matter to the task of imagining alternative methods for engaging in ecocritical theory and lived practice.
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 | 0.027 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.039 | 0.109 |
| Scholarly communication | 0.031 | 0.035 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".