Building a solid foundation for reading: an analysis of curriculum, policy, and instructional material documents
Bibliographic record
Abstract
Scientific reading research (SSR) has demonstrated that all students should be taught foundational reading skills (phonological awareness and orthography) to become fluent word readers. Curriculum/policy documents and instructional materials should reflect scientific reading research. In this study, the following research question was examined: Are there differences in the extent to which the reading-related resources (curriculum/policy documents and instructional materials), provided to two different groups of teachers—classroom teachers and resource teachers in one Nova Scotia jurisdiction—reflect evidence from the scientific studies of reading? Content analysis was used to examine the extent to which the reading-related resources (curriculum/policy documents and instructional materials), provided to two different groups of teachers—classroom teachers and resource teachers in one Nova Scotia jurisdiction—reflect evidence from the scientific studies of reading. The content analysis used two approaches to investigate evidence of SSR in the curriculum/policy documents and instructional materials—analysis using an established SSR analytical framework, and keyword analysis of the content of the materials. This study found evidence of different messaging in the curriculum/policy documents and instructional materials for classroom teachers and resource teachers. The implications of the findings for collaborative practice are discussed.
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 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 | 0.007 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".