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Understanding The Low Women Autonomy In Sidama Zone, Southern Ethiopia

2016· article· en· W2505519673 on OpenAlexaff
Eyasu Regassa, Nigatu Regassa

Bibliographic record

VenueJournal of Woman s Reproductive Health · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican history and culture analysis
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAutonomyPopulationSocioeconomicsFocus groupSimple random sampleDeveloping countryGeographyBusinessEconomic growthDemographic economicsSociologyEconomicsDemographyPolitical scienceMarketing

Abstract

fetched live from OpenAlex

The low level of women autonomy and the key pre disposing factors affecting household decision makings among many population groups in Ethiopia is not well understood among scholars, and is less investigated. This study examined the status and the micro level factors associated with women autonomy in Sidama, the most populous zone in Southern Ethiopia. A simple random sampling technique (using the available complete listing of households) was used to select the 231 sample households from one of the districts of the zone. Sidama zone was selected due to its historically strong customs of patriarchal family system. Quantitative and qualitative data were obtained using structured questionnaire and focus group discussions. Household, women and husband characteristics were used as explanatory variables while women autonomy index, developed from a set of questions, served as the dependent variable. The study revealed that women's decision makings on core household and personal issues were very low in the study population. The predicted probability, using Ordinary Least Square Regression shows that women's education, alcohol intake by husbands, household size and land size were the main determinants of autonomy in decision makings in the study area. The study recommended that concerned bodies should capitalize on educating women and girls through both formal and informal learning platforms, promote income generation activities through entrepreneurship, increased access to property and economic assets, training, microfinance and markets.

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)0.005
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.062
GPT teacher head0.312
Teacher spread0.250 · 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 designQualitative
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

Citations0
Published2016
Admission routes1
Has abstractyes

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