What Matters Most? Toward a Robust and Socially Just Science of Reading
Bibliographic record
Abstract
ABSTRACT Science of reading is a term that has been used variously, but its use within research, policy, and the press has tended to share one important commonality: an intensive focus on assessed reading proficiency as the primary goal of reading instruction. Although well intentioned, this focus directs attention toward a problematically narrow slice of reading. In this article, we propose a different framework for the science of reading, one that draws on existing literacy research in ways that could broaden and deepen instruction. The framework proposes, first, that reading education should develop textual dexterity across grade levels in the four literate roles first proposed by Freebody and Luke: code breaker (decodes text), text participant (comprehends text), text user (applies readings of text to accomplish things), and text analyst (critiques text). Second, the framework suggests that reading education should nurture important literate dispositions alongside those textual capacities, dispositions that include reading engagement, motivation, and self‐efficacy. Justification is offered for the focus on textual dexterity and literate dispositions, and we include research‐based suggestions about how reading educators can foster student growth in these areas. Finally, we propose that reading education should attend closely to linguistic, cultural, and individual variation, honoring and leveraging different strengths and perspectives that students bring to and take away from their learning. Reimagining a science of reading based on these principles has the potential to make it both more robust and more socially just, particularly for students from nondominant cultures.
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 | 0.034 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.071 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".