Flexible Enough? The Gender Gap and the Uptake of Flexible Work
Bibliographic record
Abstract
Flexible work arrangements (such as remote work, variable scheduling, variable work hours and shorter work weeks or hours) have been identified by researchers as a remedy to the pressures of traditional gender roles (such as child rearing and housekeeping) that make it harder for female workers to reach equity with their male counterparts. However, as our report notes, although women disproportionally bear the brunt of work/life responsibilities, they report having less flexibility than men in changing the rate at which they work, as well as their working hours. This lack of flexibility is more pronounced for women with young or several children. Traditionally these pressures have resulted in women being over-represented in the accommodation and food services industry. Unfortunately, this industry has also been hit particularly hard by the COVID-19 pandemic. Canada may therefore have an unprecedented opportunity to expand employment opportunities for women by taking advantage of the widespread changes to work arrangements as a result of COVID-19. Our report provides a roadmap for policymakers and employers with multiple recommendations to extend options for flexible work across all industries. These include having employers implement a “trust-based” model to evaluate their employees on their output instead their hours, and creating informal networking opportunities for remote workers.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".