Towards gender equality in the labour markets of Canada, USA and Russia: an overview of progress in achievement of international commitments
Bibliographic record
Abstract
In 2019, the International Labour Organization (ILO), together with the Organisation for Economic Cooperation and Development (OECD), prepared and presented to the G20 leaders a report entitled “Women at work in G20 countries: Progress and policy action”. According to the report, Canada, the United States and Russia show the lowest results among the G20 countries in reaching the goal of reducing the gender gap in labour force participation by 25 percent by 2025. This is largely due to the relatively high levels of gender equality that have already been achieved in these countries. The article analyzes the policy of Canada, the USA and Russia towards women at work in four directions: 1) measures taken by national Governments, in cooperation with social partners, to increase women’s participation in the labour force and to overcome cultural and behavioural barriers to the employment of women; 2) measures to increase women’s ability to earn decent wages, including through lifelong learning, upgrading qualifications and skills development; 3) measures to reduce the proportion of women employed in the informal sector and in low-paid jobs; 4) measures to protect women in labour market in order to encourage men and women to combine work and family and share family responsibilities equitably.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".