Disruption into Production: How the ‘Clean Air’ Movement Created its Own Expertise
Bibliographic record
Abstract
This study examines how the COVID-19 pandemic disrupted established expertise, triggering the emergence of new domains of expertise within the ‘Clean Air’ movement. Building on literature on social change and boundary work, we propose a production model of expertise, highlighting how crises destabilize established authority and catalyze collective efforts to produce alternative expertise. Through an in-depth exploration of coalitions advocating for clean air in Canadian schools, we identify three archetypes of expertise—scientific, functional, and experiential—each engaging in distinct practices such as aligning technical knowledge, creating standards, and assembling eclectic networks. Our findings underscore the relational and processual nature of expertise, revealing how academic, professional, and layperson groups converge to address shared social issues, reshape boundaries, and assert new forms of authority. By situating expertise within the broader context of social movements and societal change, this research advances understanding of how expertise is produced in response to a collective challenge.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.039 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".