Women’s Economic Empowerment Sans Labour Rights: Inadvertent Oversight or Tacit Omission
Bibliographic record
Abstract
Empowering women has come to be seen as a primary means to achieve women’s development in the discourse of development and policy initiatives. This has led to greater attention than before on the questions that expand the conventional understanding of empowerment itself. Much consideration has been given to answering these questions. What, however, has been ignored is the due emphasis on labour rights. The studies conducted by the feminists in the recent years have not only expanded the understanding of empowerment but have also highlighted the challenges and barriers to achieving it. Despite the fact that almost all studies have referred to the issue of weak enforcement of labour laws, the workings of development agencies do not reflect a keen commitment to this issue, in particular issue. With this backdrop, this paper reviews the existing development literature on empowerment and maps the ways in which empowerment has been measured using macro-economic indices like Gender Inequality Index (GII), Gender Disparity Index (GDI) and Gender Gap Index (GGI). It identifies the discrepancies and contradictions in the evaluations that data generated from these indices, arguing that ‘instrumentalist feminist goals,’ which are achieved through a top-down policy-level approach, are often at odds with ‘micro-level’ qualitative assessments of economic empowerment. The paper concludes with casting doubt on the compatibility of ‘development’ with women’s economic empowerment as it ignores the concomitant discussion on labour rights. It also ponders whether this omission is serving the needs of global capitalism and offers an insight into the deliberate or an unintentional oversight by the development actors.
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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 | 0.026 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.059 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".