Dissertation Data Collection During a Global Pandemic: Barking Dogs, Crying Babies, and Feminist Social Work
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this 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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it