On Accelerationism—Decolonizing Technoscience through Critical Pedagogy
Bibliographic record
Abstract
The Manifesto for an Accelerationist Politics (MAP) begins with the now numbingly routine observation that the world is facing a number of impending catastrophes stemming from the degradation of the planetary bios and the unfettered expansion of global capitalism. What makes the MAP unique from scores of other meditations on impending “ cataclysm ” is that it suggests contemporary crises are rooted largely in a failure of radical-progressive imagination. It argues that the left has simply not been able to pose a credible alternative to neoliberalism. This is because progressive politics have failed to offer alternative visions of the future and have instead stalled out, either advocating for a nostalgic return to Keynesian era social democracy that is neither possible nor desirable - (Fordism was always predicated on class, race, and gender hierarchies as well as an international system of imperialism and colonies -), and/or retreating into a horizontal politics of neo-primitive localism that is incapable of adequately dealing with real historical and political conditions, such as imagining global governance structures needed to address issues like demilitarization and climate change.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| 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".