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Record W4235875052 · doi:10.32920/ryerson.14655027.v1

The Settlement experience of Salvadoran refugees

2021· preprint· en· W4235875052 on OpenAlexaffabout
Veronica Escobar Olivo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRefugeeSettlement (finance)NarrativeContext (archaeology)NegotiationSpanish Civil WarPolitical scienceGender studiesGovernment (linguistics)Qualitative researchDisplaced personIdentity (music)Syrian refugeesPower (physics)SociologyGeographyLawSocial scienceArchaeology

Abstract

fetched live from OpenAlex

This narrative qualitative study explored the lived experiences of Salvadoran refugees who came to Canada after fleeing the civil war in El Salvador. The research aimed to examine the experience of Salvadoran refugees who arrived between 1980 and 1992. During this period, the Canadian government enacted special measures which allowed for Salvadorans to seek refuge in Canada. The experiences shared by participants explored their experience with the traumas of war, migration and eventual settlement in Toronto. The theoretical framework drew on the coloniality of power and structuration theory. These experiences were considered within a broader context of what it meant to be a Salvadoran refugee in Toronto, both in ongoing connections to their country of origin and their country of settlement over thirty years later. The narratives of the participants provide insights into the complex negotiations into the experiences of refugees forced to flee and reorient themselves in a new society. Key words: Salvadoran, refugees, experiences, civil war, identity, Latinx, Toronto

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)0.002
metaresearch head score (Gemma)0.004
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 score0.162
Threshold uncertainty score0.323

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0190.012
Scholarly communication0.0050.002
Open science0.0010.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.033
GPT teacher head0.381
Teacher spread0.347 · 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
Published2021
Admission routes2
Has abstractyes

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