Bibliographic record
Abstract
According to the World Economic Forum, countries with the strongest economies are those that have found ways to advance the careers of women, especially working mothers. Fortune 500 companies with a high ratio of women as senior executives or on the board of directors measure highest in every form of profitability. At a time where gender opportunity is making strides and the percentage of female participation in the workforce almost at parity to men, we seem to know more about parental leave from the employer’s side, surrounding predictors of retention and engagement metrics, than we do about the personal experiences of mothers in the workforce. To truly support and advance this cohort, it is important to complete the full picture of this transition by understanding the experience of taking time off work to have a child and returning to work from the women’s point of view. Through speaking with women individually and engaging in a full spectrum conversation about aspects of both work and family in this transition, this project aims to address this gap in academic literature, in order to achieve a healthier distribution of knowledge and understanding of women returning to work after maternity leave. The output of this project uses secondary and primary research to generate insights and recommendations that invested stakeholders can use to create positive impact and enhanced experiences for these women. \nKeywords: Employment, Diversity and Inclusion, Canada, Maternity Leave, Maternity Benefits, Gender
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".