DIESEL DEATH ZONES IN THE AMAZON EMPIRE: ENVIRONMENTAL JUSTICE IN ALGORITHMICALLY MEDIATED WORK
Bibliographic record
Abstract
This paper explores and contextualizes recent activism in 2019-2020 around Amazon’s San Bernardino airport warehouse expansion. While California has become a nexus for US debates on the rights of gig labour and tech workers, this coalition focused particularly on intersections of worker rights and environmental justice. The highly polluting air cargo centre, they argued, would worsen air quality and constitute environmental racism in the predominantly Hispanic, working-class San Bernardino. This coalition used creative tactics and data practices informed by place-specific histories of economic and environmental activism, to re-imagine algorithmically mediated work and link it to ongoing struggles. Analyzing primary materials and media coverage of this diverse coalition, we find a strategy unified around economic justice, environmental justice, and community benefits. This case study contributes a framework for worker-centric, site-specific analyses of internet technologies and sustainability. By exploring this intersection, we hope to provide insight into building more equitable internet infrastructures and designing technological systems in solidarity with affected communities, workers, and environments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".