<i>Eden’s Endemics: Narratives of Biodiversity on Earth and Beyond</i>. By Elizabeth Callaway
Bibliographic record
Abstract
In Eden’s Endemics, Elizabeth Callaway contributes to scholarship about the variation and extinction of life. She looks at narratives of biodiversity in twentieth- and twenty-first-century science writing, the visual culture of biology, nature writing, science fiction novels, and databases. From the opening critique of a passage by E. O. Wilson, the politics of biodiversity narratives are clearly articulated. For example, in Callaway’s archive, the Global North is often presented as the place where biodiversity is counted, studied, and protected, and the Global South as both the place where biodiversity is concentrated and the source of human threats against it. Yet, as she argues, environmental destruction often originates in colonial and economic domination by the Global North. Eden’s Endemics is a welcome product of the California or West Coast school of ecocriticism, splitting the difference between constructivism and materialism by theorizing the reciprocal production of matter and meaning. One starting point for the book is a critique of the “impulse to view biodiversity as information rather than a feature of embodied organisms” (10). What matters is embodied nonhuman agency and the coproduction of matter and meaning. Thus, Callaway taps a crucial ideological vein by stressing how the study of biodiversity often leads to disembodiment, dematerialization, and abstraction from ecological context.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.007 |
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".