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Record W7071142463

Still waters run deep: giving poetic voice to the silent struggles of academically successful immigrant students

2017· dissertation· en· W7071142463 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueeScholarship@McGill (McGill) · 2017
Typedissertation
Languageen
FieldComputer Science
TopicQR Code Applications and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationMainstreamThrivingLanguage proficiencyTheme (computing)First languageQualitative researchForeign languageHigher education
DOInot available

Abstract

fetched live from OpenAlex

ABSTRACTTypically, newcomer students to North American who must learn the school language as their second language (L2), tend to become conversationally fluent and socially adept in their L2 rather quickly (after one or two years), but continue to struggle to use their L2 in more cognitively complex academic tasks for up to five years (Cummins, 1979; 1984). Teachers who work with L2 learners in mainstream classes may be accustomed to this pattern and therefore aware of the additional academic language support that these students require. However, not all immigrant struggles are academic in nature. This study focuses on a different subset of immigrant students in North America who may fail to receive support for their needs, namely, students who have previously attended private English immersion schools in Latin America where they have already reached a high academic proficiency in their L2, but where they have had few opportunities to use their L2 socially. While thriving academically in their new schools, these students may still struggle with social isolation. For this qualitative study, I conducted semi-structured interviews with six participants who immigrated to either the United States or Canada when they were between the ages of 11 and 19 from Colombia and Mexico. They all attended private bilingual schools in their countries of origin. I used poetic inquiry to examine the common themes in their immigration experiences. Specifically, after analyzing my data for themes through open coding and poetic inquiry, I recombined fragments of individual's interviews to create 'found poetry' on each theme (Butler- Kisber, 2010), thus using participants' own voices to express their common experiences with language, culture, identity confusion, and social isolation as they adjusted to their new society (Butler-Kisber & Stewart, 2009). Based on findings from the study, I emphasize the need to heighten teachers' awareness of immigrant students' academic and social needs, and I outline measures that teachers and schools can take to support these students' social integration.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0110.001
Research integrity0.0010.002
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 head0.014
GPT teacher head0.268
Teacher spread0.254 · 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