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Record W2080743386 · doi:10.1080/19436149.2014.959796

Low-Income Islamic Women, Poverty and the Solidarity Economy in Iran

2014· article· en· W2080743386 on OpenAlexaff
Roksana Bahramitash

Bibliographic record

VenueMiddle East Critique · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSolidarityPovertyIslamPolitical scienceSanctionsSolidarity economyGovernment (linguistics)State (computer science)Private sectorPolitical economyWelfareDevelopment economicsEconomic growthEconomySociologyEconomicsLawGeographyPolitics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.256
Teacher spread0.240 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2014
Admission routes1
Has abstractyes

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