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Record W2773636617 · doi:10.1016/s2214-109x(17)30460-6

Measuring women's empowerment: a need for context and caution

2017· letter· en· W2773636617 on OpenAlexaff
Robin Richardson

Bibliographic record

VenueThe Lancet Global Health · 2017
Typeletter
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsMcGill University
Fundersnot available
KeywordsEmpowermentContext (archaeology)Human Development IndexIndex (typography)Women's empowermentSociologyPsychologyPolitical scienceEconomic growthHuman development (humanity)GeographyEconomicsWorld Wide Web

Abstract

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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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.452
metaresearch head score (Gemma)0.551
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.452
Threshold uncertainty score0.676

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4520.551
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0100.004
Bibliometrics0.0120.013
Science and technology studies0.0100.049
Scholarly communication0.0250.046
Open science0.0150.024
Research integrity0.0150.050
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.083
GPT teacher head0.298
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations22
Published2017
Admission routes1
Has abstractyes

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