Bibliographic record
Abstract
In the early 1990s, activist and poet Wendell Berry character-ized the popular use of the term “environment” as “utterly preposterous.” The word, he says, “means that which surrounds, or encircles us; it means a world separate from ourselves, outside ourselves.” In outlining how the “real state of things,” which “is far more complex and intimate and inter-esting” than an anthropocentric term like “environment” allows, Berry generates a list: “The real names of the environment,” he itemizes, “are the names of rivers and river valleys; creeks, ridges, and mountains; towns and cities; lakes, woodland, lanes, roads, creatures, and people.”1 Nearly contemporaneously, the philosopher Michel Serres also exhorted us to “forget the word environment.” “If the soiled” and endangered “world” is what we mean when we employ the term, then we have it all wrong, Serres says: this use of the term “assumes that we humans are at the center of a system of nature.” He instead proposes that “we . . . place things in the center and us at the periphery, or better still, things all around and us within them like parasites.”2 Like Timothy Morton’s “Nature” (with a cap-ital “N”), “environment,” Berry and Serres indicate, seems to be “getting in the way” of environmental work and theory.3 We would do well, then, to unlearn the term.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".