Unbalanced Nature, Unbounded Bodies, and Unlimited Technology: Ecocriticism and Karen Traviss’s Wess’har Series
Bibliographic record
Abstract
While nature is often claimed to be a space of harmonized balance or an antidote to the chaos of the modern world, we need a more grounded assessment of nature as endlessly changing and much less predictable than we like to assume. In this essay, I explore Karen Traviss’ provocative exploration of unbalanced nature and unbounded bodies in her wess’har series with the guidance of two ecocritics who reject the concept of balanced nature, Dana Phillips and Ursula Heise. Additionally, I turn to the environmental philosopher Val Plumwood for insights regarding Traviss’ spurious yet rather standard vision of an unlimited technological panacea. Traviss’ series portrays how the boundaries and limits that we perceive as solid are often much less so than we believe, yet she also reveals—inadvertently, it seems—how easily we blindly ignore other, more solid limits.
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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 | 0.003 | 0.005 |
| 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 | 0.041 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".