The double‐X factor: harnessing female human capital for economic growth
Bibliographic record
Abstract
Purpose The purpose of this paper is to present MEDA programs in small enterprise development – value chain and microfinance projects – to illustrate that the human capital of even hard to reach women can be harnessed for a community's and even a nation's economic growth. Design/methodology/approach The paper provides evidence, in the form of case studies, demonstrating that with a relatively low investment of resources, women are empowered to contribute to the growth of the small business sector which is a cornerstone of a robust private sector. Findings Working with disadvantaged populations that have been relegated to the bottom of the socio‐economic heap has challenges. Program design must overcome a host of constraints including illiteracy and innumeracy, lack of technical and business skills, and the psycho‐social consequences of generations of disenfranchisement. Yet, case after case has proven that these seemingly intractable obstacles diminish with appropriate project interventions. Whether or not the program “targets” or “mainstreams” women should be based on the context and an understanding of women's situation. MEDA has had varying degrees of success with these two approaches, and preliminary findings suggest that value chain projects derive greater benefit from targeted activities than do microfinance programs. The impact of business women's efforts extends well beyond their own businesses, to finance family enterprises, educate children, improve household nutrition, organize community groups, and build more equitable social structures. These “indirect” benefits of the economic empowerment of women will serve any nation committed to the growth of trade and commerce. In fact, some would argue, the authors included, that they are necessary conditions of sustainable wealth creation. Originality/value The paper presents original case material from a number of MEDA value chain and microfinance projects, both past and present, to illustrate the concept, and share project design and implementation learnings.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".