The Distances that the Covid-19 Pandemic Magnified: Research on Informality and the State
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
What does research on informal sector workers and the state entail in the time of Covid-19? The pandemic has limited possibilities for in-person interactions and required adaptations in research approaches. These challenges are exacerbated when the subjects of the research are informal sector workers with limited access to technology and undefined spaces of work. In this article, we argue that the Covid-19 pandemic has magnified distances: between researchers located globally; between researchers and respondents; and between the state and people within informal employment. However, these distances also create new ways of working and opportunities for doing research. We discuss the challenges faced in the field, document the adaptations introduced to ensure robust research in difficult settings, and set out the limitations that remain. We also examine the ethical dimension of confronting dangerous misinformation related to the pandemic while conducting interviews, and the questions it raises about the distance between research and prescriptive advocacy in academia.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it