The Distances that the Covid-19 Pandemic Magnified: Research on Informality and the State
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".