Bibliographic record
Abstract
Our relationships to the environments that surround, sustain, and sometimes threaten us are fraught with emotion. And since, as neurologist Antonio Damasio has shown, cognition is directly linked to emotion, and emotion is linked to the feelings of the body, our physical environment influences not only how we feel, but also what we think. Importantly, this also holds true when we interact with artistic representations of such environments, as we find them in literature, film, and other media. For this reason, our emotions can take a rollercoaster ride when we read a book or watch a film. Typically, such emotions are evoked as we empathize with characters while also inhabiting emotionally the storyworlds that surround these characters and interact with them in various ways. Given this crucial interlinkage between environment, emotion, and environmental narrative in the widest sense, it is unsurprising that, from its inception, the study of literature and the environment has been interested in how ecologically oriented texts represent and provoke emotions in relation to the natural world. More recently, ecocritical scholars have started to develop a more sustained theoretical approach to exploring how affect and emotion function in environmentally oriented texts of all kinds. In this article, I will attempt to trace this development over time, briefly highlighting some of the most important texts and theoretical concepts in affective ecocriticism
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".