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Record W2338500740 · doi:10.1515/hssr-2016-0003

Women’s Status among Households in Southern Ethiopia: Survey of Autonomy and Power

2016· article· en· W2338500740 on OpenAlexaffabout
Nigatu Regassa, Gete Tsegaye

Bibliographic record

VenueHuman and Social Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsUniversity of SaskatchewanUniversity of Regina
FundersHawassa University
KeywordsAutonomySocioeconomicsIndex (typography)Administration (probate law)Quarter (Canadian coin)GeographyDemographyEconomic growthDemographic economicsPolitical scienceSociologyEconomicsLaw

Abstract

fetched live from OpenAlex

Abstract This study examined two key dimensions of women’s status (autonomy and intimate partners violence) in Southern Nations, Nationalities and Peoples Region (SNNPR) of Ethiopia based on regional data collected from five randomly selected zones and one city administration; namely, Sidama, Hadya, Gamo Gofa, South Omo, Bench Maji and Hawassa City Administration. The analysis revealed that while joint decision is fairly high, women’s independent decision making on key household domains is generally low. Significant proportions of women in the region are exposed to violence by their partners ranging from insult to heavy physical injury. The fact that nearly half of the women experienced insult and close to a quarter of them faced beating is indicative of the low status of women in society. The regression analysis indicated that seven variables determine the occurrence of violent acts against women in the study area, namely household size, education, access to radio, value of children index, wealth index and level of women autonomy. On the other hand, decision making autonomy is affected by wealth status, household size, access to radio and sex preference index. Finally, the study highlighted the importance of addressing the limited technical and operational capacities to implement gender policy and legal frameworks effectively and efficiently.

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)0.001
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 score0.253
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.120
GPT teacher head0.340
Teacher spread0.220 · 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

Citations5
Published2016
Admission routes2
Has abstractyes

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