The Unseen Landscape of Abolitionism: Examining the Role of Digital Maps in Grassroots Organizing
Bibliographic record
Abstract
Prison and police abolition has become a major political philosophy in North American discourse following the 2020 George Floyd protests. The philosophy remains divisive, and North American abolitionists seeking to coalition-build, provide resources for vulnerable populations and garner public support continue to experience challenges. We explore current usage of digital tools among abolitionists and the potential of a digital mapping tool to address these challenges. We conduct an interview study with 15 abolitionist organizations to understand activists' perspectives on the value of digital tools for organizing and a content analysis of 25 existing digital tools that convey abolitionist ideas to the public. Our findings together reveal (1) opportunities for digital mapping and HCI to support abolitionist activism and grassroots activism more broadly and (2) the challenges of digitally and spatially representing a movement that is intentionally grassroots, clandestine, and often involves organizers working in disparate locations.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".