Extensions after Man: Race, Counter/insurgency and the Futures of Media Theory
Bibliographic record
Abstract
This article returns to the geopolitical scene and racial logics that provide the underacknowledged conditions of Marshall McLuhan’s Understanding Media and, specifically, its well-known proposition that media should be understood foremost as ‘outerings’ or ‘extensions of man’. Attending to the structuring inheritances of racial slavery and the plantation system in this founding statement of mid-twentieth-century media theory, as well as its debt to the literary and intellectual movement of the Southern Agrarians, I consider how the racializing figure of ‘Man’ conserved by the nascent field of media studies was contemporaneously brought to crisis by black (and) anticolonial freedom struggles. Arguing for the need to reread the career of western media theory through its political vocation in attempting to manage this crisis, the article concludes by turning briefly to a revisionary account of media and exteriority also circulated in 1964: the revolutionary intellectual James Boggs’s ‘The Negro and Cybernation’. Boggs’s writings, which situate emergent forms of computing and cybernation within a longer materialist genealogy of race, capitalism and technology, offer both a proleptic critique of the early disciplinary formation of media theory and a divergent set of coordinates for approaching media technology on the terrain of black political struggle.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.057 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".