Recognizing The Right to Care: A Key Step in Building the Care Economy
Bibliographic record
Abstract
The care crisis stems from the social prescription of women as caregivers, leading to their overrepresentation in unpaid care work. Unpaid is linked to broader structural crises like economic inequality, poverty, and precarity, which are exacerbated by intersecting inequalities. The care crisis perpetuates women’s marginalization in economic and social spheres due to the invisibility of care work in the care economy. The care economy includes non-market and market-based care provision, involving public investments in social infrastructure and protection. According to the International Labour Organization, women perform 76.2% of all unpaid care work globally, dedicating 3.2 times more hours than men. In Latin America, women spend 6.3 to 29.5 more hours per week on unpaid care work than men, totalling 8,417 million weekly hours. Similar trends are observed in Canada, where women undertake more unpaid care work and face greater well-being impacts. The COVID-19 pandemic highlighted these issues, creating a policy window to elevate care as a political and social policy priority. This research conducts a comparative policy literature review between Canada and Uruguay— focusing on developing and enforcing the right to care and implementing a National Integrated Care System to address unpaid care work. The research aims to provide actionable recommendations for Canada. Key questions include: What are the advantages and disadvantages of recognizing care as an autonomous right? How can recognizing care as a right help achieve the 5Rs––Recognize, Reduce, Reward, Represent, and Resilience? The welfare state has three pillars: health, social security, and education. Introducing the right to care challenges this model by acknowledging care as a fundamental but previously unrecognized and unenforced pillar. The proposed recommendation is for the Canadian government to follow the pursuit of Uruguay in formulating, adopting and implementing an autonomous right of care in collaboration with multiple stakeholders and creating a National Integrated Care System.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".