The Intersection of Motherhood and Academia During a Pandemic: A Storytelling Approach to Narrative Oral History
Bibliographic record
Abstract
This paper takes a storytelling approach to narrative oral history using reflexivity as analysis, making meaning through social engagement between co-authors, friends, family, and colleagues. The story presents the first author's lived experience as a mother and academic, both journeys at their peak as the pandemic loomed closer to and arrived in Canada. These journeys and their intersection are presented in chronological order, detailing the stressors and struggles of mothering in academia during a pandemic. The second author played an integral role in telling this story, by drawing out the narrative through an open-ended interview. Reflexive thoughts, authentic accounts, and interview quotes are embedded throughout conveying lived experience, feelings, and concerns. The paper magnifies structural gender inequality in academia by sharing struggles, such as loss of opportunity for scholarly contributions, pregnancy secrecy and career advancement anxieties, the reality of maternity “leave” in academia, and accounts of personal support and lack of professional support. We hope this piece gives mothers in academia comfort in knowing they are not alone in work-life challenges, encourages women in similar positions to share their stories, opens the academic world to these lived realities, and inspires equity-informed change for the good of mothers and academia.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.031 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".