BORDERLINE CARE AND POLITICS: THE EVERYDAY EXPERIENCES OF WORKING MOTHERS IN BORDER COMMUNITIES DURING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
The COVID-19 pandemic caused widespread disruption to people as they tried to navigate the uncertain and ambiguous public health approaches of governing institutions. Conflicting guidelines made it challenging to comply with public health practices, as they were often developed with a single institutional entry point in mind rather than for parents and caregivers navigating complex and interrelated institutional recommendations and restrictions for more than themselves. This thesis explores how working mothers adapted to modifications in institutional care support as socially constructed during the pandemic. Institutional Ethnography (IE) was employed to generate qualitative data on the lived experience of working mothers during the pandemic. IE helps identify the ruling relations in institutional practices such as emergency management. This study analyzes the impact of emergency management on women's workforce participation during crises. It examines historical and legislative frameworks and includes interviews with working mothers from cross-border communities of Ottawa and Gatineau, and Windsor and Detroit to understand their experiences. This thesis broadly concludes with the aim to shed light on how individuals changed during the dynamics of the pandemic. It highlights the emotional labour output of working mothers who had to juggle the demands of their jobs with the added responsibilities of caring for their families. This output led to exhaustion and burnout, both personally and professionally. Working mothers could produce this work with innovation by reconciling the tensions of the first and second shifts with a third shift to coordinate the tensions between the first and second shifts. To better understand working mothers' challenges, policymakers and legislators must acknowledge their responsibilities and develop frameworks that provide support. Modernized institutional frameworks will help to take in consideration the social and economic risks so to not perpetuate unintended negative consequences on the labour force.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".