Meat My Hero: “I have a Dream” of Living Language in the Work of Donna Haraway, Or, Ride ‘Em Cowboy!
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it