Bad nature how rat control shapes human and nonhuman worlds
Bibliographic record
Abstract
"Offers insights into the social and cultural implications of humans' relationships with rats and the natural world. Apart from the occasional pet owner who has rats, most people regard rats as disease-carrying nocturnal pests, scurrying around dumpsters and dragging slices of pizza through New York City subways. Since rats are seemingly omnipresent in human life, why do we harbor such negative feelings about them, and why are they among the creatures most frequently targeted for systematic extermination? In Bad Nature, sociologist Andrew McCumber draws out the cultural underpinnings of rat extermination across three countries and two continents. Drawing from ethnographic, interview, and textual data from the frigid prairie of Alberta, Canada; the heart of downtown Los Angeles, California; and the iconic Galápagos Islands of Ecuador, McCumber studies how humans have sought to suppress and exterminate rat populations in a variety of environmental, social, and political situations. He shows how, in these disparate locations, rat control is a social practice that draws and clarifies the spatial and symbolic boundaries between "good" and "bad" forms of nature. Rats are near the bottom of a symbolic hierarchy of species that places human life at the top, companion animals and majestic wildlife just below them, and the "invasive species" that call for systematic extermination at the very bottom. This hierarchy of living things that places rats at the bottom, McCumber argues, mirrors human systems of social inequalities and power dynamics. Both original and engaging, Bad Nature urges readers to consider, when charting a just and sustainable future, where will the rats be placed in the worlds we envision?"--
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".