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Record W2488136558 · doi:10.1075/ni.25.2.01val

Making sense of immigration processes

2015· article· en· W2488136558 on OpenAlexaboutno aff
Berta Vall, Luis Botella

Bibliographic record

VenueNarrative Inquiry · 2015
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationNarrativeFriendshipAcculturationPsychologySample (material)Quality of life (healthcare)PunctuationDevelopmental psychologySocial psychologyHistoryLiteratureArtComputer science

Abstract

fetched live from OpenAlex

This article analyses the narrative disruption processes and quality of life of adolescent immigrants in Spain. Furthermore, it also provides a new methodological approach to assess meta-subjective and narrative quality of life. Participants were 30 adolescents (15 immigrant and 15 autochthons) selected form a sample of 884 adolescents (from which 204 were immigrants). Data regarding quality of life was collected applying the Friendship Quality Scale and the Vancouver Index of Acculturation to all the participants (n = 884). According to the punctuation of the questionnaires a subsample was chosen, the Biographical Grid was applied to 30 participants; the immigrants group was also asked to write a text. Results indicate that both perceived quality of life and self-esteem of immigrant’s group are lower than the autochthons’ while narrative disruption is higher. A deeply explanation about some of the causes of these results is provided by the narratives’ analysis.

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)0.009
metaresearch head score (Gemma)0.023
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 score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0030.009
Scholarly communication0.0080.013
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.175
GPT teacher head0.434
Teacher spread0.259 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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