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Record W1764006014 · doi:10.18357/ijih.101201513202

An Intersectionality Analysis of Gender, Indigeneity, and Food Insecurity among Ultrapoor Garo Women in Bangladesh

2014· article· en· W1764006014 on OpenAlexafffundvenue
Jenny Munro, Barbara Parker, Lynn McIntyre

Bibliographic record

VenueInternational Journal of Indigenous Health · 2014
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of CalgaryLakehead University
FundersCanadian Institutes of Health Research
KeywordsIndigenousPovertyIntersectionalityFood insecuritySocioeconomicsPoliticsLivelihoodGender studiesEthnic groupState (computer science)Economic growthPolitical scienceFood securitySociologyGeographyAgricultureAnthropology

Abstract

fetched live from OpenAlex

Across a number of indicators, Indigenous women worldwide experience poorer health and less access to resources and services than either non-Indigenous women or Indigenous men. This paper is specifically concerned with Indigenous women’s experiences of food insecurity, a key determinant of health. We analyse the links between gender, indigeneity, and food insecurity among a small group of ultrapoor Indigenous Garo women in Bangladesh. We demonstrate how the politics of indigeneity, both its erasure by the state and its place in the Indigenous rights movement, intersect with gender to shape the experiences of food insecurity among ultrapoor rural Garo women. We identify how indigeneity intersects with cultural marginalization and political violence in experiences of food insecurity and highlight the unique challenges Garo women face in relation to accessing culturally appropriate and sufficient food. Ultrapoor Garo women are in need of poverty alleviation attention from both Indigenous organizations, who market their cultural capital, as well as governmental and non-governmental aid and development programs that offer such assistance to vulnerable Bengali women.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
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 head0.019
GPT teacher head0.316
Teacher spread0.297 · 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 designObservational
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

Citations11
Published2014
Admission routes3
Has abstractyes

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