Research Adaptivity in Times of Disruption: Zig-Zagging Your Way through the Field During the COVID-19 Pandemic
Bibliographic record
Abstract
ABSTRACT This study reflects on the field research interruptions that occurred around the world with the onset of the COVID-19 pandemic. Based on my experience of in-person and remote fieldwork with vulnerable populations and sensitive research topics during this time, I introduce a “zig-zagging approach” that can be used as a research adaptivity strategy in times of disruption. I argue that “zig-zagging your way through the field” is a legitimate strategy as long as researchers acknowledge that changing from in-person to remote fieldwork (and vice versa) will alter various aspects of their relationship with the field including;(1) perception of positionality and authenticity; (2) processes of trust building and security challenges; and (3) experience of ethnographic immersion and observation. I offer mitigation strategies to reduce the impact of change and also discuss aspects that cannot be mitigated when working with vulnerable populations or sensitive research topics. I conclude on why going back—and forth (i.e., zig-zagging)—should become a practical solution when all else fails.
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 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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".