Bibliographic record
Abstract
Taking the myth of Noah’s ark as “our most enduring narrative for how to survive life during climate change” (vii), Jeffrey J. Cohen and Julian Yates trace the biblical story from its bare-bones account in Genesis through countless permutations from the medieval to the modern. The result, Noah’s Arkive, is itself an ark: at once a “a failed … totality” (20)—it cannot account for every ark variant—and a rich array of multimedia ark stories, including the Middle English poem Cleanness, Sun Ra’s Space is the Place, Gustav Doré’s The Deluge, and two Playmobil sets. These artifacts, Cohen and Yates contend, demonstrate ark thinking, or “what an ark means and how it asserts that meaning through what it collects and excludes” (4). To describe Arkive as failed is to honor its success. Cohen and Yates locate in the Noachic ark’s violent exclusion both “ethical wrongness” and impossibility (285): “There are no architectures of exclusion that are not already full of uninvited bodies and narratives” (175). Following Donna Haraway and Anna Lowenhaupt Tsing, the authors locate arks’ “vitality” in the very “portals and porosities” that guarantee their failure (58, 327; 65). The ecological promise of the ark is not in its programmatic conservation but in the more-than-human communities and improvised ecosystems that Noah always thinks he has foreclosed.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.585 | 0.442 |
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".