Essays on job satisfaction, minimum wage, and work from home : exploring gender, household, and policy influences on labour market outcomes in Canada
Bibliographic record
Abstract
Among the most salient topics in today’s labour market are the issues of job satisfaction, minimum wage, and the rise of work from home. Firstly, labour market studies have sought to understand job satisfaction in relation to gender pay gaps. The empirical findings are often seen as a paradox as they indicate that women tend to have high job satisfaction despite having lower earnings. Secondly, minimum wage reforms have gained more attention as various jurisdictions have moved towards higher minimum wages. There is interest in assessing the effects of these relatively substantial wage reforms, in particular regarding their effects on youth employment. Thirdly, work from home has gained popularity following its large-scale adoption in response to the COVID-19 pandemic shock. There is growing interest in understanding the effects of these shifts towards flexible work arrangements, and whether work from home may help reduce gender differences in labour supply and earnings. The aim of this thesis is to improve understanding of the gender, household, and policy mechanisms that impact differences in labour market outcomes in relation to these salient topics. The thesis utilizes Canadian labour market data and provides timely insights which are of key relevance to workers, employers, and policy makers alike.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".