A Framework for a Culturally Sustaining “Science of Reading”
Bibliographic record
Abstract
Abstract For decades, the “reading wars” have unfolded between two paradigms: a holistic, meaning‐oriented approach, and a cognitively oriented skills‐based approach. The “wars” have long complicated educational research, policy, and practice. Yet, neither the holistic nor the cognitive paradigm has historically centered strengths of linguistically and culturally diverse communities. This article uses theory‐building to explore these paradigms and illuminate the prismatic dimensions of culturally sustaining reading instruction. We propose a reconciliatory bridging framework that builds on students' and educators' strengths and lived experiences. At the crux is teachers' deep professional learning, to effectively cultivate students' linguistic and literary repertoires across and within languages, dialects, cultures, identities, and histories. Teachers' assessment literacy and multidisciplinary knowledge are key, as is greater pluralization of the social sciences. Limitations within high‐stakes accountability regimes are also discussed.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.062 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".