Low-Income Islamic Women, Poverty and the Solidarity Economy in Iran
Bibliographic record
Abstract
This article is based on fieldwork research among Islamic women of low and lower middle-income households and seeks to explore their role in the solidarity/social economy, which is neither part of the government nor the private sector and primarily is rooted in the community. It documents the female-dominated, mainly informal solidarity economy functioning parallel to official and semi-formal public and private ways that Iran deals with poverty. The research concentrates exclusively on women who are practicing Muslims, as the bulk of the data I gathered was from low and lower middle-income neighborhoods where religion is a part of daily lives and where there are more incidents of economic need. The solidarity economy among the low income demonstrated that the role of religious women is important. Moreover, because of an overall decline in the role of the state, especially the development (welfare) state globally but more specifically in this case Iran, as well as the economic crisis due to international sanctions, the solidarity economy is a vehicle for poverty relief. Self-identified Muslim women's roles in the solidarity economy tend to remain by and large invisible and undocumented and therefore its value overlooked. Yet, as literature on gender and development indicates, women's role in distributing resources is geared toward their families and communities and therefore understanding how their networks operate can provide policy insight into how poverty can be addressed through community and grass roots organizations, some of which operate in the form of self-help, while others mediate between the public and the private sector.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".