Theory That Matters: Feminism, Porous Boundaries and the New Materialisms
Bibliographic record
Abstract
Ocean, watching BBC news footage of the tsunami aftermath in Japan. Shock and mourning over lives literally swept away have been somewhat eclipsed by trepida-tion over a nuclear power plant on the edge. The latest effort to mitigate this impending disaster involves military helicopters and gallons of seawater to pacify the restless fuel rods. Clearly, “nature ” is hardly a passive backdrop for human cul-ture. But while the tsunami itself underlines the hubris of human attempts to keep the sea, the weather, and the earth’s geology at bay, the looming threat of nuclear meltdown also reminds us that it is never a case of “humans ” versus “the environ-ment. ” What is around us also travels through us. Between nature and culture, between “out there ” and “up close, ” between toxic environments and our inner selves, between the city and the sea—these boundaries are always an open question. Responses to these events continue to come in many forms. Critical and cultural theory, too, struggles to make sense of such porous borders, as well as the very real persistence and power of matter in relation to human subjects. While over the past decades poststructuralist theories of discursive construction have brought invalu-able insights, they have also engendered concern over the effacement of material embodiment, agency and the complex ways in which the matter of our environ-ments (a rogue wave, a radioactive milieu) matters. In response, we have seen the T O
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.123 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".