Gender Equality in the Workplace: Women's Perspective
Bibliographic record
Abstract
The present paper investigates gender equality in employment. It aims to understand which factors fuel gender gaps and which drivers could overcome them to build an inclusive and fair working environment. In high-income countries, female employment has been rising steadily for decades. However, average employment levels among women remain lower than those of men and considering parents with young children, the situation is even more emphasized. To achieve the research objective we administered a structured quantitative survey questionnaire to a sample of women from Italy and Canada. From the analysis, it emerged that there are some differences between the Italian and Canadian women's points of view. Specifically, the latter seem more satisfied with their country's support for women's work than Italian women. Moreover, both samples believe that working and having economic independence is fundamental in a woman's life. At the same time, however, they perceive that having a family could affect their career growth. Respondents pointed out that tools such as kindergartens, paternity leave, childcare incentives, and smart working can be valuable tools to support female work. Despite the many advances, greater effort is required to support women's empowerment and overcome gender barriers by promoting more equality in the workplace.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".