Improving the Measurement of Women’s Work: The Contribution of Demographic Surveys in Francophone West Africa
Bibliographic record
Abstract
Since the 1970s, the measurement and recognition of women’s work and their contributions to family well-being and economic development have been a matter of academic interest, as well as feminist advocacy. The interrelationships between women’s work and demographic processes, especially decisions regarding fertility, have also attracted the attention of demographers for some time. However, despite long-standing efforts to capture all aspects of women’s work, large-scale demographic and economic surveys conducted in the Global South still fail to approach work as a gendered concept and continue to make much of women’s labor invisible. The measurement of unpaid care and household responsibilities is particularly scarce. In such a context, the purpose of this chapter is twofold. First, it retraces the long history of the global efforts of feminist scholars and activists to enhance the measurement of women’s work. Second, it illustrates how recent data collection initiatives in francophone West Africa, building on the experience of collaborative research conducted by demographers in the region since the 1970s, have attempted to fill some of the persisting gaps in data on women’s activity, especially on household domestic and care, voluntary collective/community work, as well as on women’s contribution to household expenses and family support.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".