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Record W2981401952 · doi:10.29173/spectrum68

Canadian Citizens’ Helping Intentions toward Syrian Refugees

2019· article· en· W2981401952 on OpenAlexaffvenueabout
Mahnoor Khan, Leah K. Hamilton

Bibliographic record

VenueSpectrum · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsMount Royal UniversityUniversity of Calgary
Fundersnot available
KeywordsRefugeeSyrian refugeesEmpathyPerspective (graphical)ImmigrationContext (archaeology)Social psychologyIdentity (music)Ethnic groupPolitical sciencePoliticsPsychologySociologyLawGeography

Abstract

fetched live from OpenAlex

Abstract Since 2011, over 5 million refugees have fled civil war in Syria (UNHCR, 2018). Canada has responded tothe Syrian refugee crisis by resettling over 50,000 Syrian refugees and encouraging its citizens to supportthe integration process. Previous research has shown that when in-group members take the perspectiveof an out-group it leads to increased helping intentions toward that out-group (Mashuri, Hasanah,Rahmawati, 2013). To replicate and extend these findings in a Canadian context, the current study soughtto answer the question: How does national identity impact the relationship between perspective taking andhelping intentions toward Syrian refugees? The results indicated that when undergraduate participantsengaged in perspective taking, it led to increased financial helping intentions toward Syrian refugees,and this relationship was mediated by empathy. It was also found that individuals with a greater ethnicnational identity had lower levels of political and financial helping intentions toward Syrian refugees. Keywords: Syrian Refugees, Immigrants, Newcomers, Canada, National Identity, Ethnic National Identity, Perspective Taking, Empathy, Helping Intentions

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.018
GPT teacher head0.311
Teacher spread0.292 · 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

Citations3
Published2019
Admission routes3
Has abstractyes

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