COVID-ized Ethnography: Challenges and Opportunities for Young Environmental Activists and Researchers
Bibliographic record
Abstract
This article offers a critical and reflective examination of the impact of the enforced 2020/21 COVID-19 lockdown on ethnographic fieldwork conducted with UK-based young environmental activists. A matrix of researcher and activist challenges and opportunities has been co-created with young environmental activists using an emergent research design, incorporating a phased and intensive iterative process using online ethnography and online qualitative interviews. The article focuses on reflections emerging from the process of co-designing and then use of this matrix in practice. It offers an evidence base which others researching hard-to-reach youth populations may themselves deploy when negotiating face-to-face fieldwork approval at their own academic institutions. The pandemic and its associated control regimes, such as lockdown and social distancing measures, will have lasting effects for both activism and researchers. The methodological reflections we offer in this article have the potential to contribute to the learning of social science researchers with respect to how best to respond when carrying out online fieldwork in such contexts—particularly, but not only, with young activists.
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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 | 0.073 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".