Bibliographic record
Abstract
Out of the gusts of creative energy following the 2013 publication of Nick Srnicek and Alex Williams’ “#Accelerate: Manifesto for an Accelerationist Politics,” the cyber-feminist collective, Laboria Cuboniks, published their own manifesto in 2015. Entitled “The Xenofeminist Manifesto: A Politics for Alienation,” Laboria Cuboniks advocated, broadly speaking, the abolition of gender, increased technological intervention into the means of re-production, and, most controversially, an affirmation of alienation as intrinsically liberatory. Met with mostly positive responses, the Xenofeminist Manifesto spawned a series of workshops, talks, and accelerationist adjacent theorizing. That being said, residual issues of humanism and an open question about what “more alienation” actually means festered just below the surface. In response to recent articles critiquing Xenofeminism as misunderstanding Marxist-Transhumanism at best, and reifying white feminism at worst, the following article seeks to do three things. First, I aim to examine the neo-humanisms (be they trans- or post-humanism) that occupy our current era of technocapital acceleration, and sketch out a critique that affirms the inhuman; Second, I attempt to trace the accelerationist lineage of Xenofeminism by looking at early Marx up to Deleuze and Guattari while noting that Xenofeminism can be read as a necessary outgrowth of accelerationism insofar as Xenofeminism seeks to deterritorialize gender as such; and Third, I aim to respond to recent critiques levied against Xenofeminism that claim its affirmation of alienation is not only a naïve mis-reading of Marx, but a reification of oppression. While certainly not the last word, I hope this article spawns deeper intellectual theorizing about Xenofeminism and its implications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.074 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".