Meat My Hero: “I have a Dream” of Living Language in the Work of Donna Haraway, Or, Ride ‘Em Cowboy!
Bibliographic record
Abstract
Meaning is the product of dialectical negotiations of competing meanings that have their origins in cultural, subcultural, and idiosyncratic differences. Below obvious, surface, or dominant understandings, latent meanings wait to bubble up. This dynamic process of meaning-making suggests that language is, to a certain degree, uncontainable and very lively. Donna Haraway's work can be characterized by an attention to this 'latency' in language. I argue that Haraway’s use of language is not merely a way of communicating ideas, but constitutes a methodology, theory and praxis all at once, because she obtains “data” by mining latency, because she theorizes the significance of undercurrents and assumptions in phenomena, and because her writing itself demonstrates the very latency she is keen to explore. Here, language demonstrates an immensely generative capacity, such that we can understand language as being “living” – perhaps a companion species, and not merely dead “meat.” Through an analysis of American meat culture and what I call “meat heroism," I mime the infinite recursion in Haraway’s work, adopting her praxis in order to illuminate her praxis in order to illuminate her method which illuminates her theory. This paper is about language, failure, humour, cowboys, hero sandwiches, Martin Luther King Jr., and protein.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".