Rethinking human capital: Perspectives from women working in the informal economy
Bibliographic record
Abstract
Abstract Motivation The development of human capital is a priority for most nation states, accelerated by the COVID‐19 global pandemic. In the context of reimagining a “new normal” post‐COVID, we reconsider the concept of human capital, and focus on knowledge, skills, and training of individuals in order to capture aspects of inclusive development. Purpose This paper shows how the perspective of women, informal sector workers, representing some of the most marginalized workers in society, informs and improves our understanding of human capital and its development and utilization. Methods and approach Our findings are derived from field‐based research conducted over the summer of 2021 in which multiple (virtual) focus group discussions (FGDs) were held with selected members of the Self‐Employed Women's Association (SEWA) in India. Findings Through our FGDs, the participants provided new perspectives and insights into our knowledge of human capital, emphasizing the importance of social protection programmes, gender equity, ongoing training opportunities, decentralized supply chains, and income security. Perhaps most significantly, the benefits accrued to women through being organized have been key to unlocking their human capital potential. Policy implications Our research highlights themes that are often overlooked in the literature or are beyond the scope of more narrow conceptualizations of human capital. We show that human capital is tightly interwoven with other forms of capital (community assets), and hence efforts to build the former cannot be achieved in isolation from attending to the latter.
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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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.026 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".