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Record W3132512144 · doi:10.11575/prism/37104

Identity in Resettlement: Perspectives of Female Refugee Lone Parents from Africa

2019· dissertation· en· W3132512144 on OpenAlexaboutno aff
Lucy Karimi Amadala

Bibliographic record

VenueOpen MIND · 2019
Typedissertation
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeIdentity (music)Gender studiesGenealogyPolitical scienceSociologyCriminologyHistoryLawArt

Abstract

fetched live from OpenAlex

The purpose of the current study was to explore how the identities of women from Africa who are resettled in Canada as refugee lone parents might be influenced by the resettlement experience. There is extensive research on resettlement outcomes for newcomers (Frank, 2013; Guo, 2013; Painter, 2014; Schwartz et al., 2010), including those resettled as refugees (George, 2012; Huijts, et al, 2012; Teixiera & Dias, 2018), but not much is known about what happens to individuals' identity as they settle in the new countries. Given this, I explored identity change in the context of resettlement for this specific class of refugees, by asking the question: How do refugee single mothers from Africa make sense of how their identities have changed or remained the same in the context of settling in Canada? In conducting this study, I was guided by a qualitative research method, Interpretative Phenomenological Analysis (IPA; Smith, Larkin, and Flowers; 2009). Using purposive and snowball sampling, I recruited nine women living in two major urban centres in Alberta, who met criteria for participation. I conducted audio-taped, in-depth, semi-structured interviews with each, in either Kiswahili or English. I translated and transcribed the ones in Kiswahili to English and transcribed the ones in English verbatim. Following this, I analyzed the data according to IPA procedures (Smith et al, 2009). Through this process, I created five super-ordinate themes related to identities that remained the same; that had been lost; that were acquired in resettlement; that might change in the future; and that others might use to describe this population. The findings are explained and discussed in the context of relevant literature. Strengths and limitations of the study are considered as well as implications of the findings for research, policy, and the practice of counselling with this population.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.075
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0210.013
Scholarly communication0.0070.005
Open science0.0020.007
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.041
GPT teacher head0.395
Teacher spread0.354 · 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 source (direct Gemma or distilled Codex), 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
Published2019
Admission routes1
Has abstractyes

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