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Record W2615909294

Culturally Relevant and Responsive Practices in Literacy Instruction

2017· article· en· W2615909294 on OpenAlexaboutno aff
Denessa Nicole Ricketts

Bibliographic record

VenueTSpace · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLiteracyPedagogySociology
DOInot available

Abstract

fetched live from OpenAlex

This qualitative study focused on investigating the ways in which educators utilise culturally relevant and responsive practices (CRRP) in order to improve their literacy instruction. Through a detailed literature review, as well as direct, in- person or telephone, interviews with three experienced teachers from various school boards in the Greater Toronto Area, four important themes emerged. The first theme: recognising, celebrating and learning from cultural diversity in the classroom; describes the need for safe and respectful settings for cultural diversity to be acknowledged. The second theme: teaching literacy skills to students; educators should give their students engaging tasks to spark interest in literacy. The third theme: using culturally relevant and responsive practices to develop literacy; educators should utilise cultural texts within their instruction. The fourth theme: teacher identified challenges (and benefits) to the approach; emphasises that teachers may face lack of support and resources when trying to integrate CRRP into their instruction, but positive relationships are built with students and their families as a result. Through interviewing educators, it was concluded that these practices could be utilised in classrooms but teachers would still need proper training and support. There would be a call for culturally relevant and responsive changes within the current curriculum and proper professional development for educators to be properly prepared to meet the cultural and literacy needs of their students. Based on the findings, this research project expresses how educators can use culturally relevant and responsive practices to guide their own teaching and improve their literacy instruction.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.075
GPT teacher head0.497
Teacher spread0.422 · 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
Published2017
Admission routes1
Has abstractyes

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