Why does precarious work matter?: the implications of precarious work on job and life satisfaction in Canada
Bibliographic record
Abstract
The goal of this study is to understand how precarious work relates to job and life satisfaction. Additionally, this research aims to describe who is most likely to experience aspects of precarious work based on their socio-demographic characteristics and what attributes of precarious work can be found in five main industries. To answer these questions, I used the 2016 Canadian General Social Survey (cycle 30). I analyzed the dataset in SPSS and STATA. I analyse descriptive data using crosstabulations and comparing means. For the multivariate analysis I use both logistic regression analysis and ordinary least squares regression analysis. The findings from the research show that as the number of indicators for precarious work increases for men and women, those who work in precarious jobs are significantly more likely to report lower job satisfaction than those who do not work in precarious jobs. Finally, for each additional precarious work indicator, men report a decrease in their life satisfaction, but the relationship between precarious work and life satisfaction is not significant for women. Furthermore, women, younger people, those who identify as a visible minority, those who are Indigenous, those who are recent immigrants, and those with lower levels of education are significantly more likely to experience aspects of precarious work. Also, aspects of precarious work are most likely to be found in the accommodation and food services and retail trade industries. Overall, this research explores precarious work in Canada and how it relates to job and life satisfaction.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".