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Record W2924159685

Research Literacy in Canadian Initial Teacher Education Programs

2019· article· en· W2924159685 on OpenAlexaffabout
Angela Vemic

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicEducational Assessment and Improvement
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsPracticumLiteracyAccreditationPedagogySociologyQualitative researchTeacher educationMathematics educationPolitical sciencePsychologySocial science
DOInot available

Abstract

fetched live from OpenAlex

Teachers benefit greatly from the ability to critically read and conduct research on their own teaching practice. However, teacher candidates often complete ITE programs with many of the same beliefs surrounding research that they held upon entering (Gitlin, et al. 1999; Joram, 2007). Despite the need for a carefully structured approach to research literacy, little attention has been paid to how research literacy is integrated into ITE programs in Canada. This study uses qualitative document analysis of publicly accessible faculty of education websites to better understand where and how research literacy is integrated into accredited university ITE programs across Canada (Angers and Machtmes, 2005; Bowen, 2009; Fairclough, 2003; Valli, 2000). Tentative results indicate that while most ITE programs state the goal of building an inquiry stance among students, a significantly smaller number provide tangible research education components throughout the program. While some programs offer research courses or independent primary research projects, nearly no programs offer the opportunity for students to conduct research as an integrated part of their in-school practicum experience. Findings will offer a valuable resource to universities, policy makers, and researchers seeking to improve research literacy programing and better understand the state of teacher education in Canada.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.173
GPT teacher head0.481
Teacher spread0.308 · 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 designObservational
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
Published2019
Admission routes2
Has abstractyes

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