The influence of workplace context on fathers’ use of parental leave in Canada
Bibliographic record
Abstract
Much research has examined fathers’ use of parental leave in the international context, focusing on the role of state policies and/or the influence of the family in shaping fathers’ leave decisions. Missing from these analyses is an examination of how the workplace context might shape fathers’ leave use. The current thesis attempts to fill this gap by investigating variation in fathers’ leave use and leave length in Canada as these relate to cultural and structural features of the workplace context. Using data from the nationally representative Survey of Labour and Income Dynamics, I run logistic regression and negative binomial regression to test the effects of occupational culture and structural features such as workplace sector and size on fathers’ use and length of leave, respectively. Results indicate a positive and significant effect for management and science-related occupations on leave use but this effect disappears upon the introduction of individual-level control variables. Other work-related predictors include large workplaces and having a permanent job, both of which positively and significantly predict leave use. Length of leave was not found to be related to workplace context. These findings point to the importance of structural features of the workplace in shaping fathers’ use of leave, but not necessarily the length of their leave.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".