Precarious Work and COVID-19: A Mixed Methods Analysis
Bibliographic record
Abstract
The COVID-19 pandemic constitutes an unprecedented public health, social and economic crisis that acutely highlights both the precariousness of temporary work and the essential nature of some precariously employed workers. No previous studies have longitudinally examined the trajectories of temporary and permanent workers’ weekly wages during the pandemic or leveraged qualitative analysis to examine workers’ lived experiences of the pandemic’s impacts. To address this research gap, the study employed a mixed methods approach. Quantitative analysis of Labour Force Survey data from January 2019 to September 2021 was conducted to examine weekly wage trajectories of temporary versus permanent Canadian workers during the pandemic as well as the extent to which several factors (e.g., type of occupation, industry, age, gender) influenced these trajectories. Data were also leveraged from the Bank of Canada COVID-19 stringency index, a measure of government policy responses. In addition, qualitative content analysis of 30 print news articles from March 2020 to September 2021 was employed to better understand workers’ lived experiences of wage and employment impacts. Quantitative analyses revealed that temporary workers experienced greater weekly wage losses than permanent workers, particularly in the early months of the pandemic. Surprisingly, among temporary workers, subgroups who would generally be considered more advantaged (male, older, more highly educated and higher-wage workers), experienced greater wage losses in the early months of the pandemic and in association with increasing stringency index values. This suggests that female, younger, less educated and lower-wage temporary workers, were more likely to be concentrated in essential jobs. The various data-driven and theoretically informed frames applied in the qualitative analysis highlighted how workers’ voices shaped the construction of meaning surrounding their lived experiences. Their voices furthered understanding of the impacts and structural causes of their employment precarity as well as solutions needed to address work precarity and the social and economic inequalities worsened by the pandemic. Both the quantitative and qualitative findings highlighted how previously undervalued jobs emerged as essential, leading to a re-valuation (at least temporarily) of these occupations. This study draws attention to the critical need to address employment precarity at both the national and global levels.
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.
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 | 0.069 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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, 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".