In Conversation with Vicki Kirby: Deconstruction, Critique, and Human Exceptionalism in the Anthropocene
Bibliographic record
Abstract
Abstract In this interview Marc Higgins invites Vicki Kirby to dilate on the themes that have exercised her attention over the last thirty years. His questions address the received assumptions that shape political and ethical debate and the suggestion that their terms of reference require a radical shake-up. Kirby’s counter-intuitive treatment of familiar and accepted ways of thinking pays special attention to the nature/culture division and its myriad reconfigurations (body versus mind; primitive, or first, versus complex, or second; illiteracy versus literacy). She interrogates the routine and almost automatic logic that segregates what is deemed abstract and ideational from the pragmatic gravitas and political urgency that we tend to secure in empirical, “on the ground” evidence. For Kirby, this notion of material evidence and the weight of its truth claims, together with the corollary belief that the ideational and abstract are entirely other to physical and material reality, promote an insidious political agenda that sustains misogyny, racism, and ecological degradation as inevitable. By underlining the implicated ecologies of life whose dynamic cross-overs and impurities are also manifest in our thought structures, we are challenged to work with/in a sense of corruption that is irreducible and not simply negative.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.057 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".