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Record W4404558282 · doi:10.36941/mjss-2024-0049

Heritage Nation Educational and Vocational Paths

2024· article· en· W4404558282 on OpenAlexaboutno aff
Erinda Papa

Bibliographic record

VenueMediterranean Journal of Social Sciences · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeVocational educationImmigrationHeritage languageFace (sociological concept)Cultural heritagePolitical sciencePedagogySociologyEconomic growthPublic relationsPsychologyGender studiesSocial scienceLaw

Abstract

fetched live from OpenAlex

The intake of immigrants and refugees by countries such as Canada, the US, the UK, and Australia has significant implications for involved adults and children. As they navigate their heritage nations, the acquisition of the English language becomes essential for education and vocational purposes. In essence, to be integrated into their heritage nations, migrant adults and children face the challenge of having to learn a second language. It is important to acknowledge the disruption of education and careers experienced by these individuals as they seek new beginnings in heritage nations. While heritage nations such as Canada have programs that facilitate the integration of immigrants and refugees, some of the initiatives fail to take into account their diverse needs. For example, Fang et al. (2018) indicate that refugees face gendered barriers, low education levels, emotional scars and physical impairments, and cultural barriers, which hinder their acquisition of the English language. Understanding the specific challenges immigrants and refugees face as they transition to their new environment is critical to improving their experience. This paper examines the psychological, socio-cultural, and educational implications of education and career disruptions of immigrants and refugees and suggests strategies to support new language acquisition. Received: 30 September 2024 / Accepted: 2 November 2024 / Published: 20 November 2024

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.811
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.379
Teacher spread0.325 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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