Dissertation Data Collection During a Global Pandemic: Barking Dogs, Crying Babies, and Feminist Social Work
Bibliographic record
Abstract
COVID-19 has had a profound impact on our society. Research evidence has surfaced that there is a gender disparity in research productivity due to COVID-19. Notably, women in academia have been less productive in terms of academic publications since the beginning of the pandemic, likely due to the day-to-day responsibilities of childcare and domestic work; and according to pre-print literature, women of color may be more significantly impacted. As a woman of color, PhD candidate, mother of a toddler, wife, advocate for mental wellness, researcher, and social worker, reflecting on these recent articles was quite disheartening. Additionally, the impact of COVID-19 lockdowns on doctoral students has had detrimental impacts on our ability to collect data we need to forge our paths through this academic journey. This in-brief paper is written in response to the numerous questions I have been asked by other doctoral students around how I collected 41 in-depth, semi-structured interviews while working from home during a global pandemic, with my toddler at home with me. I reflect on how I pivoted to recruit participants, scheduled interviews, and conducted interviews from home, and how I believe COVID-19 has created space for a more accessible qualitative data gathering experience.
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".