Teacher’s Role in the Reading Apprenticeship Framework: Aid by the Side or Sage by the Stage
Bibliographic record
Abstract
Despite decades of efforts, alarming statistics about the literacy crisis from secondary school teachers indicate that the reading abilities of the learners are inadequate for the materials to be taught and teachers wonder if adolescents are literate enough, language-wise, to leave school and enter colleges or universities.The common mode of teaching allows students a passive role in class which leads to their being disengaged from literacy. How we teach literacy is of great importance if students are to become empowered as lifelong readers. As individuals differ in their reading abilities, teachers must move beyond testing for comprehension if students are to embrace a new way of being literate.Although research has taught us much about what is needed to read, it has provided much less knowledge about effective means of helping students learn to read. This study hoped to design a literacy program to respond to this need through a Reading Apprenticeship Framework as a partnership of expertise, drawing on what the teacher knows and does as a reader and on pre- university students’ often underestimated strengths as learners using exploratory mixed method design.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".