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Record W2927937179

The education and integration of immigrant children within Ontario: A content analysis with policy implications

2018· article· en· W2927937179 on OpenAlexaboutno aff
Camila Lara, Louis Volante

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationProsperityPolitical scienceMulticulturalismEconomic growthContext (archaeology)Diversity (politics)Social integrationChristian ministrySocial policyGeographyEconomics
DOInot available

Abstract

fetched live from OpenAlex

Known as the land of immigrants , Canada continues to attract thousands of immigrants every year - not only for its open migration policies and multicultural views, but because of the rights and opportunities that the country provides to newcomers. For this reason, federal and provincial governments must continuously develop policies that can successfully manage diversity and contribute to the economic and social prosperity of the Canadian society. Within the larger context of migration, the education and integration of children of immigrants within school systems has become of pressing concern for policymakers. With children of immigrants under the age of 15 representing over 37 per cent of all Canadian children, the establishment of policies that support immigrant students’ transition and integration have become particularly important. Such policies are meant to remove certain barriers and disadvantages experienced by children of immigrants in order to reduce the achievement gap between migrant and non-migrant students.  Through a content analysis of Ontario’s Ministry of Education policy documents, this research examined whether current provincial policies, procedures, and strategies provide immigrant students the educational support measures that they need to successfully integrate within school systems.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.034
GPT teacher head0.323
Teacher spread0.290 · 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.

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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venue2019 Conference of the Canadian Society for the Study of EducationSame topicEducation and experiences of immigrants and refugeesFrench-language works237,207