Assessing Unpaid Care Work: A Participatory Toolkit
Bibliographic record
Abstract
This is a participatory toolkit for understanding unpaid care work and its distribution within local communities and families. Together, these tools provide a way of ascertaining and capturing research participants’ understanding of women’s unpaid care work – giving special attention to the lived experiences of carrying out unpaid care work and receiving care. Please note that these tools were developed and used in a pre-Covid-19 era and that they are designed to be implemented through face-to-face interactions rather than online means. We developed the first iteration of these tools in our ‘Balancing Care Work and Paid Work’ project as part of the Growth of Economic Opportunities for Women (GrOW) programme. The mixed-methods project sought to collect data across four countries – India, Nepal, Tanzania, and Rwanda – with data collected in four sites in each country (16 sites in total). The participatory tools were developed with two main intentions: (1) as a data collection tool to gain a broader understanding of the social norms and perspectives of the wider community in each of the 16 sites; and (2) to be implemented with our local partners as a sensitisation tool for the community regarding women’s unpaid care work burdens. While it is not essential to apply these tools in the order that they are presented, or even all of them, we would suggest that this toolkit be used in its entirety, to gather in-depth knowledge of social norms around the distribution of unpaid care, and the impacts that these have on care providers’ lives and livelihoods.
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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 | 0.098 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".