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Record W2784473293 · doi:10.11575/prism/5403

Cultivating Literacy Engagement in Multilingual and Multicultural Learning Spaces

2018· dissertation· en· W2784473293 on OpenAlexaboutno aff
Theodora Kapoyannis

Bibliographic record

VenuePRISM (University of Calgary) · 2018
Typedissertation
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsMulticulturalismLiteracyMultilingualismPedagogySociologyMathematics educationLinguisticsPsychologyPhilosophy

Abstract

fetched live from OpenAlex

Classrooms are changing rapidly in response to Canada’s linguistically and culturally diverse demographic profile. Learning in the 21st century calls for inquiry into different ways of designing curriculum, teaching, conducting assessments and educational research than is found in K-12 education today. In this doctoral research, I examined the impact of a curricular innovation to challenge the monolingual and monocultural norms of literacy practices and to be responsive to the linguistic and cultural landscape of 21st century classrooms. Using design based principles and a mixed methods approach to data collection and analysis, I collaborated with 11 university pre-service teachers to bridge theory and practice to inform early literacy practices for 28 students who are learning English, i.e., English Language Learners (ELLs). The primary question framing this study was, “How can educators cultivate literacy engagement to support English language development?” The findings of this study show the positive impact the designed literacy intervention had on supporting the linguistic and cultural needs of the young ELLs as well as how the implementation of the literacy intervention supported the pre-service teachers’ emerging practice. Through the design based research process, I created design principles to inform early literacy practices for young ELLs.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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