Assessing Impacts of the COVID-19 Pandemic on Anthropological Research Methods
Bibliographic record
Abstract
As the COVID-19 pandemic continues, there is a need to understand how the pandemic has influenced anthropological research. This paper presents the results of a research project examining these changes and the challenges anthropologists have faced in carrying out their research methods during the first eight months of the COVID-19 pandemic. At the University of Northern British Columbia in the Fall semester of 2020, undergraduate students led this project and conducted five virtual, semi-structured interviews with socio-cultural anthropologists across Canada, from a variety of career stages and with diverse research approaches. Interview participants described virtual research methods involving a heavy reliance on video conferencing and digitally available resources, benefits and challenges of remote and digital ethnography, changes to immersion and the spatial-temporal aspects of communication, and outcomes of adopting new technologies. The pandemic affected these anthropologists to varying degrees depending on the location of their field site and their career stage. Despite adaptations and challenges, interview participants also offered hopeful commentary on potential long-term changes in the discipline as the pandemic forces anthropologists to rethink the ways in which we conduct our work.
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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 | 0.335 | 0.231 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.026 | 0.034 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.006 | 0.035 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier 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".