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Record W4206643945 · doi:10.1177/17454999211057449

Cultural alloys and heterogeneous mixes: Contextualized and comparative language differences in literacy assessment of U.S. and Canadian youth

2022· article· en· W4206643945 on OpenAlexaboutno aff
S. Joel Warrican, Melissa L. Alleyne, Patriann Smith, Rahat Zaidi, Tala Karkar Esperat, Yi‐Hsin Chen, Yue Yin

Bibliographic record

VenueResearch in Comparative and International Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLiteracyMulticulturalismImmigrationReading (process)Context (archaeology)Language proficiencyIdentity (music)Neuroscience of multilingualismSociologyPsychologyGender studiesPedagogyPolitical scienceHistory

Abstract

fetched live from OpenAlex

The United States and Canada, two countries known to have large immigrant populations, have long since reflected a dichotomy, where Canada is generally perceived to be a country with language policies that demonstrate its receptiveness to embrace multiculturalism in schools and classrooms. In contrast, the United States has consistently espoused the notion that one is “American first” and one’s cultural identity follows behind. It is within this context that the following study examines the difference in reading literacy performance between youth in the U.S. who self-identify as native English speakers and those who self-identify as non-native English speakers on the PISA assessment. The study also explores the difference in reading literacy performance among Canadian youth who self-identify as native English speakers, those who self-identify as native French speakers, and those who self-identify as neither native English nor native French speakers on PISA. Implications for policy, practice and society 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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.464
GPT teacher head0.622
Teacher spread0.158 · 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 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
Published2022
Admission routes1
Has abstractyes

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