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Record W2297777589 · doi:10.1111/lit.12074

Teacher expectations and student literacy engagement and achievement

2016· article· en· W2297777589 on OpenAlexaff
Sylvia Pantaleo

Bibliographic record

VenueLiteracy · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsNarrativeSituatedPsychologyLiteracyPedagogyMathematics educationNarrative inquiryLinguistics

Abstract

fetched live from OpenAlex

Abstract Notwithstanding the complex and dynamic nature of teaching and learning in schools, over four decades of research findings have consistently revealed a correlation between teacher expectations and student achievement. Focusing on teacher expectations for the narrative structures created by young children, this article features a discussion of data gathered during a multifaceted study with 7‐ and 8‐year‐old students. The overall purpose of the case study research was to explore the development of student understanding of elements of visual art and design and diverse narrative structures in picturebooks. For the culminating activity of the research, the students had opportunities to apply and transform their knowledge of the instructional foci when they composed their own multimodal print texts. Analysis of the students' narrative structures revealed the multiple forms of metalepsis, the purposeful breaking of storyworld/narrative boundaries, evident in their writing and artwork. In addition to a discussion about the socially situated nature of the children's multimodal text‐making, the article includes a consideration of the importance of teacher expectations with respect to student literacy engagement and achievement.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.027
GPT teacher head0.297
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2016
Admission routes1
Has abstractyes

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