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Record W4214566398 · doi:10.1080/15575330.2022.2042346

“We don’t talk about Trauma”: El Salvadorians and Community Trauma

2022· article· en· W4214566398 on OpenAlexafffundabout
Mirna E. Carranza, Ken Moffatt, Susan McGrath, B. Lee

Bibliographic record

VenueCommunity Development · 2022
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsToronto Metropolitan UniversityYork UniversityMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDiasporaIndigenousSociologyGender studiesColonialismNegotiationPovertyRacismHistorical traumaMulticulturalismSpanish Civil WarPolitical scienceCriminologySocial scienceLawPsychology

Abstract

fetched live from OpenAlex

This article draws upon data from a Canadian study funded by the Social Sciences and Humanities Research Council. The focus was on how trauma has been experienced by three communities who have been historically marginalized: The El Salvadorian community, Indigenous peoples and those identifying as 2STLGBQIA+. The focus of this article is on the Salvadorian diaspora in Canada, which has the shared experience of historical colonial trauma; on-going coloniality leading to civil war; and poverty, leading to forcible displacement. The latter resulted in re-negotiating their lives in the diaspora and transnationally in El Salvador. As with Indigenous and 2STLGBQIA+ communities, Salvadorians in Canada share experiences of multiple oppressions and marginalization based, in part, on perceptions of belonging and worth, rooted in racism. Exploring notions of community development as it exists in diaspora communities must include a history of war, resettlement and marginalization that contribute to trauma at the community level.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.491
Threshold uncertainty score0.976

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0210.023
Scholarly communication0.0070.004
Open science0.0020.009
Research integrity0.0020.004
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 head0.056
GPT teacher head0.330
Teacher spread0.274 · 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 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

Citations1
Published2022
Admission routes3
Has abstractyes

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