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Record W7079626598 · doi:10.26108/a6am-pt10

The impact(s) of South Sudanese cultural marriage practices on women's pursuit of post-secondary education in Canada

2022· article· en· W7079626598 on OpenAlexaboutno aff

Bibliographic record

VenueAcadiaU-DEV · 2022
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)RefugeeInterviewFeminismFocus groupEthnographyCultural diversityAcculturation

Abstract

fetched live from OpenAlex

The central question asked in this thesis is "How/does South Sudanese cultural marriage practices impact South Sudanese women's pursuit of post-secondary education in Canada?" This thesis, therefore, aims to investigate how cultural marriage practices impact South Sudanese women in their quest for post-secondary education. This study focuses on the experiences of six South Sudanese women who immigrated to Canada as refugees. The focus is drawn toward their educational aspirations and their cultural experiences. To provide context for this research, literature on migration, the making of a refugee, the resettlement process, assimilation, refugee experiences in host societies, and refugee women's experiences are broadly discussed. The research findings explain the effects of South Sudanese cultural marriage practices on South Sudanese women who have immigrated to Canada as refugees. Using African Feminism as a theoretical perspective, the study explores the importance of centring African women's voices and experiences with the aim of creating gender equality. Data collection methods incorporated in this research are semi-structured interviewing and autoethnography, as they provide an in-depth analysis of the study. The study results show how South Sudanese cultural marriage expectations can coexist with women's desire to pursue post-secondary education.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.981

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.255
Teacher spread0.245 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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