Worlding beyond ‘the’ ‘end’ of ‘the world’: white apocalyptic visions and BIPOC futurisms
Bibliographic record
Abstract
We often hear that the ‘end of the world’ is approaching – but whose world, exactly, is expected to end? Over the last several decades, a popular and influential literature has emerged, in International Relations (IR), social sciences, and in popular culture, on subjects such as ‘human extinction’, ‘global catastrophic risks’, and eco-apocalypse. Written by scientists, political scientists, and journalists for wide public audiences, 1 this genre diagnoses what it considers the most serious global threats and offers strategies to protect the future of ‘humanity’. This article will critically engage this genre to two ends: first, we aim to show that the present apocalyptic narratives embed a series of problematic assumptions which reveal that they are motivated not by a general concern with futures but rather with the task of securing white futures. Second, we seek to highlight how visions drawn from Black, Indigenous and People of Color (BIPOC) futurisms reimagine more just and vibrant futures.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.048 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".