Encouraging Sight Vocabulary Among Developing Readers
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".