MétaCan
Menu
Back to cohort
Record W7074020944

Belonging among Newcomer Youths: Intersecting Experiences of Inclusion and Exclusion

2010· article· en· W7074020944 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Resources Studies
Canadian institutionsnot available
Fundersnot available
KeywordsInclusion (mineral)Context (archaeology)Inclusion–exclusion principleIntersectionalitySocial exclusionResistance (ecology)ImmigrationConstruct (python library)Refugee
DOInot available

Abstract

fetched live from OpenAlex

Belonging has been identified as an important resource for health and well-being in the lives of youths. Thus, it is an important concept for upstream health promotion and culturally safe and relevant nursing care. While many researchers acknowledge the importance of the social, cultural, and political context in the lives of newcomer youths, little research has examined the sociopolitical processes inherent in immigrant and refugee youths' experiences of belonging. By employing an intersectional and postcolonial perspective, this study explored newcomer youths’ gendered, racialized, and class experiences of inclusion and exclusion that ultimately influenced their sense of belonging in their country of resettlement. Through an examination of online blogs in the United States, the United Kingdom, Australia, and secondary analyses of transcribed interviews from a previous study conducted in Canada, experiences of belonging were revealed to be shaped by complex and multifaceted structures of oppression. Through individual and collective efforts of resistance and resiliency, newcomer youths worked to construct a sense of belonging in their daily lives. Despite these participants’ demonstrated strengths, it is evident that more work is needed to support newcomer youths’ sense of belonging and well-being throughout resettlement. Implications for nursing practice and research are discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.065
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.075
GPT teacher head0.287
Teacher spread0.211 · 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 teacher head, 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

Citations4
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueScholarship@Western (Western University)Same topicWater Quality and Resources StudiesFrench-language works237,207