Virtual Qualitative Research Using Transnational Feminist Queer Methodology: The Challenges and Opportunities of Zoom-Based Research During Moments of Crisis
Bibliographic record
Abstract
In this paper, we discussed our experiences with Zoom-based virtual qualitative research with Asian international students attending Canadian universities. When reflecting on our study, we drew inspiration from Roberts et al., (2020) who highlight the ethical challenges that emerge when conducting virtual qualitative research with a community that is experiencing the harrowing effects of COVID-19 in real time. Yet we also departed from such work by considering the added ethical complexity of conducting research during COVID-19 with research participants and with research team members who have transnational lives. In answering the question, “how do you design a virtual qualitative research project with research participants and with a research team whose lives are transnational,” we discussed how our use of transnational feminist queer methodology allows us to emphasize accountability and flexibility and recognize the multiple-and-varied social locations of our research participants and our research team members. We realized that working with research participants who have transnational lives means that notions of risk and consent cannot only be considered from the standpoint of the individual who is participating in the project. Instead, it is paramount that risk and consent be considered from the standpoint of the individual’s larger, transnational community and location in global, geopolitical contexts. Transnational feminist queer methodology also allowed us to see the challenges and possibilities of virtual qualitative research. While Zoom presented challenges (namely, that our participants were concerned about their privacy), we found the functionalities of Zoom to enhance our research. Specifically, we found that the chatbox deepened participant engagement through the sharing of memes and GIFs, allowing more rapport to develop. Ultimately, we argue that virtual qualitative research is not an inferior alternative to in-person research but should instead be seen as a different way of doing research, one necessitating distinct methodologies and methods.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.115 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.048 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".