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Record W7000289555

Encouraging Sight Vocabulary Among Developing Readers

2016· article· en· W7000289555 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Commons - East Tennessee State University (East Tennessee State University) · 2016
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionPretextArticular cartilage damageNasalizationSparganosisHyporeflexia
DOInot available

Abstract

fetched live from OpenAlex

Nicole Wilton is the program manager of the Community Music Education Program at the University of Saskatchewan. Huili Hong and Renee Rice Moran are assistant professors who teach literacy classes in the Department of Curriculum and Instruction (CUAI) at East Tennessee State University. LaShay Jennings is a clinical instructor in CUAI who works with field-based student teachers. Ed Dwyer is a professor who teaches literacy classes in the same department. Nicole, Huili, Renee, LaShay and Ed have a great deal of interest in integrating artistic, social, and experiential strategies within instructional programs designed to enhance literacy achievement. According to these experts, students need to become physically, experientially, and emotionally as well as academically involved when learning sight words and in learning in general. Emphasis is placed on encouraging teachers and other instructional personnel to foster self-efficacy among their students through activities that generate success through products produced and learning experienced. Preparing a sturdy and attractive book focused on sight vocabulary in context is presented herein as a key strategy for both promoting self-efficacy and enhancing reading competence among students in the primary grades. Although the activity presented focuses on promoting sight word acquisition among primary grade readers, the strategies are adaptable to a wide variety of learning endeavours.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0000.004
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.016
GPT teacher head0.208
Teacher spread0.191 · 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 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
Published2016
Admission routes1
Has abstractyes

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