LANG6148-CO.Critical Literacy in Elem Schl.Sp17.McGee,Kevin
Bibliographic record
Abstract
Hazel Brown and Brian Camboune first published "Read and Retell" in 1987. The book has continued to sell, and has been used by generation of teachers and researcher on both sides of the Pacific. Revisit the retelling strategy and explore ways in which it has been extended by teachers in today's classrooms. Briefly explore the principle underpinning the original "Read & Retell" initiative through a mix of interactive online teaching, hands-on exploration of variations of the procedure, simple action-research in your own classroom, and face-to-face discussion and sharing. Learn how Australian, Canadian, and U.S. teachers, consultants, and reading specialists have adopted and used the strategy in a range of classrooms. Witness how the strategy can be modified and used to help students across a range of grade levels. Special emphasis will be on the value of retelling as an assessment tool that can provide accurate and reliable data on students' growth across a range of literacy areas. Target audience: educators K-6.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.610 | 0.450 |
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".