From radiation to air pollution: Infrastructural manoeuvring within citizen environmental monitoring
Bibliographic record
Abstract
Responding to the absence of reliable radiation data in the immediate aftermath of the Fukushima Daiichi nuclear disaster in 2011, a group of citizens formed a network—Safecast— to monitor and map radiation in Japan. The emergence and politics of such a citizen monitoring network has been extensively documented (Brown et al. 2016; Polleri 2019; Van Oudheusden and Abe 2021). Scholars have also paid attention to the openness of the data and systems that Safecast and others have developed (Hemmi and Graham 2014). Drawing from the studies of infrastructures (Star 2002; Tsing 2011; Appel, Anand, and Gupta 2015; Harvey and Knox 2015), this paper asks: what sustains a large-scale citizen radiation monitoring infrastructure? To answer this question, this paper documents the maintenance work involved in sustaining citizen built environmental monitoring infrastructures. It analyses Safecast’s recent infrastructural manoeuvres to open its monitoring networks to measure and map air pollution. How, and why are radiation monitoring infrastructures opened to other kinds of environmental monitoring? Using embedded, participant ethnographic research into Safecast’s ongoing data collection and mapping, this paper approaches citizen radiation monitoring as an infrastructural site consisting of people, artefacts, data, flows, and other elements which can help to sustain an environmental monitoring network and make it durable. What makes citizen-built monitoring infrastructures durable? How do these agents—both human and non-human—help to make Safecast (and other citizen science networks) more durable over time? What helps it to avoid falling apart? Why are existing monitoring infrastructures opened to other kinds of environmental monitoring at all? Looking into citizen-sustained maintenance work, this paper investigates the impacts of opening citizen-built radiation monitoring infrastructures.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".