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Record W1989556446 · doi:10.1080/15283488.2014.944695

Managing Identity in the Face of Resettlement

2014· article· en· W1989556446 on OpenAlexaffabout
Secil E. Ertorer

Bibliographic record

VenueIdentity · 2014
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsYork University
Fundersnot available
KeywordsIdentity (music)DistressRefugeeConfusionPsychologySocial identity theoryIdentity crisisSocial psychologyFace (sociological concept)Personal identityIdentity formationEmotional distressSocial identity approachSociologyGender studiesSocial groupPolitical scienceSelf-conceptClinical psychologyAnxietyPsychoanalysisPersonalitySocial scienceLawPsychiatry

Abstract

fetched live from OpenAlex

Refugees who are resettled in third countries may be at greater risk of experiencing identity problems, such as identity distress, crisis, and its resolution, than are their nonrefugee, nonimmigrant peers. A multidimensional approach was employed to explore the identity (re)formation and resolution of 50 Karen refugees who were resettled in London, Ontario, Canada. Problematic identity processes in social, personal, and ego domains of identity were examined. Fifty nonrefugee Canadians served as comparisons. The findings revealed that the resettlement process impaired the sense of temporal sameness and continuity, promoted confusion and crisis in identity, prolonged identity resolution, and stimulated distress concerning social and personal identity issues (work, career, values, group loyalties) for the refugees who participated in this study.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.144
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.005
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.388
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

Citations27
Published2014
Admission routes2
Has abstractyes

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