Literacy principles as a tool for policy analysis: An examination of the values embedded in science of reading advocacy documents
Bibliographic record
Abstract
Debates around the Science of Reading have often been couched in consideration of research scholarship. However, before a meaningful dialogue centered on empirical evidence can fruitfully take place, there must be some clarity around shared literacy values, and around how those values might be addressed through instruction. Drawing on internationally accepted definitions and a long tradition of literacy scholarship, we identify a literacy values framework, along with 12 interdependent principles describing what literacy education should nurture and develop: capacity for communication; knowledge and understanding of the world; repertoires of purpose; capacity for understanding text; capacity for text composition; imagination and creativity; empathy; capacity for criticality; multimodal, embodied, and technological capacities; capacity for democratic citizenship; empowering literate identities; and requisite skills. An analysis using these principles was applied to two Science of Reading advocacy documents: The Right to Read report from Canada and The Reading Guarantee from Australia, revealing significant gaps in the types of literacy instruction being addressed and emphasized for literacy teaching and learning.
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 | 0.074 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.014 | 0.064 |
| Scholarly communication | 0.030 | 0.024 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".