States of prison abolition: COVID-19 and anti-colonial and anti-racist organising
Bibliographic record
Abstract
Until recently, carceral and penal logics have proliferated the global scene unabated. The coronavirus pandemic not only ushered a moment of pause for the world, but in some areas, even a reversal in carceral trends. In many countries, some sectors experienced unprecedented reductions in imprisonment and migrant detention. Even where the pandemic advanced more invasive carceral controls, such as with policing through health checks and issuing tickets, it also fuelled global resistance through the Black Lives Matter movement. In the wake of the pandemic, an uprising of activists, advocates and supporters captured the public imagination with anti-racist and abolition uprisings and advances in community care. In the lands now known as Australia and Canada, where the criminalisation and incarceration of Indigenous people has been increasing, this mobilising has resulted in important alliances and advancements to challenge these carceral and penal trajectories. In this article, we trace several abolitionist initiatives to show how the convergence of COVID-19 and anti-racist and anti-colonial movements catalysed an important moment for abolitionist organising.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".