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.
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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 | 0.062 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.035 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| 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".