Measuring women's empowerment: a need for context and caution
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Abstract
Fernanda Ewerling and colleagues,1Ewerling F Lynch JW Victora CG van Eerdewijk A Tyszler M Barros AJD The SWPER index for women's empowerment in Africa: development and validation of an index based on survey data.Lancet Glob Health. 2017; 5: e916-e923Summary Full Text Full Text PDF PubMed Scopus (115) Google Scholar in developing an index for measuring women's empowerment, provide a novel contribution to a complicated issue. The empowerment literature is fraught with controversy over both conceptual and measurement issues. Nevertheless, there is general agreement that economic and social context play a crucial role in how empowerment should be measured.2Kabeer N Resources, agency, achievements: reflections on the measurement of women's empowerment.Dev Change. 1999; 30: 435-464Crossref Scopus (1928) Google Scholar, 3Malhotra A Schuler SR Women's empowerment as a variable in international development.in: Narayan D Measuring empowerment: cross-disciplinary perspectives. The World Bank, Washington, DC2005: 71-88Google Scholar, 4Mason KO Smith HL Women's empowerment and social context: results from five Asian countries. Gender and Development Group, World Bank, Washington, DC2003Google Scholar Unfortunately, the index developed by Ewerling and colleagues overlooks these important dimensions. Ignoring context can lead to biased measurement. Ewerling and colleagues' survey-based women's empowerment index (SWPER) includes some indicators that are likely to be predicted by the context in which women live (ie, women's employment, educational level, and frequency of reading the newspaper or listening to the radio). For instance, women living in remote areas with limited employment opportunities are less likely to secure employment. The use of these indicators to compare levels of empowerment across countries is problematic because empowerment scores will conflate empowerment with basic economic and development conditions (eg, population-level employment). Separating out these two concepts is a thorny issue, and some empowerment scholars advocate for delineating indirect measures of empowerment (eg, education, employment, and media exposure) from direct measures (eg, agency, and decision-making authority).4Mason KO Smith HL Women's empowerment and social context: results from five Asian countries. Gender and Development Group, World Bank, Washington, DC2003Google Scholar Direct evidence is less context-dependent, and thus is likely to provide a better comparison of empowerment across contexts. Ewerling and colleagues advocate for applying the factor loadings derived from their principal component analysis to calculate domain-specific empowerment scores. They state that these scores can be used to compare empowerment levels throughout the African continent. However, without empirical evidence that these factor loadings are consistent across African countries, such an approach is ill-advised. Indicators that denote empowerment in one setting might not in another,2Kabeer N Resources, agency, achievements: reflections on the measurement of women's empowerment.Dev Change. 1999; 30: 435-464Crossref Scopus (1928) Google Scholar, 4Mason KO Smith HL Women's empowerment and social context: results from five Asian countries. Gender and Development Group, World Bank, Washington, DC2003Google Scholar and factor loadings can be different even in neighbouring countries.5Agarwala R Lynch SM Refining the measurement of women's autonomy: an international application of a multi-dimensional construct.Social Forces. 2006; 84: 2077-2098Crossref Scopus (78) Google Scholar There is some indication that factor loadings were inconsistent in this study: for two of the three domains, items with loadings above 0·300 were different across countries. Thus, applying the same factor loadings across countries is likely to lead to biased measurement. A preferred approach would be to only include items with loadings that are consistent across contexts. Measuring empowerment is difficult and comparing empowerment across contexts is especially challenging. Ewerling and colleagues should be commended for tackling such a difficult topic. However, more work must be done to develop comprehensive and accurate measures of empowerment that are comparable across settings. I declare no competing interests. The SWPER index for women's empowerment in Africa: development and validation of an index based on survey dataThe index, named Survey-based Women's emPowERment index (SWPER), has potential to widen the research on women's empowerment and to give a better estimate of its effect on health interventions and outcomes. It allows within-country and between-country comparison, as well as time trend analysis, which no other survey-based index provides. Full-Text PDF Open AccessMeasuring women's empowerment: a need for context and cautionSustainable Development Goal 5 (SDG5) urges governments to monitor progress towards gender equality and empowering women and girls. Improved measurement is needed to meet this mandate, which requires that women's empowerment be well defined, adequately measured by use of representative and focused samples, and statistically comparable across countries, years, and social groups. Accordingly, it is unclear whether the survey-based women's empowerment (SWPER) index, reported in The Lancet Global Health by Fernanda Ewerling and colleagues (September, 2017),1 improves measurement of SDG5. Full-Text PDF Open AccessMeasuring women's empowerment: a need for context and caution – Authors' replyWomen's empowerment is a complex concept, with no consensus on its definition or on the domains that compose the construct.1 Thus, it is expected that any attempt to measure empowerment will have limitations and will not satisfy all parties interested in the topic. However, we know that an attribute that is not measurable or measured tends to be overlooked. The Sustainable Development Goals (SDGs) raised the need for a measure of women's empowerment so that it can be monitored and compared between contexts and stakeholders made accountable. Full-Text PDF Open Access
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it