November 2006 • ACIE Newsletter • The Bridge Peer Tutoring Literacy ProgramTM • Chipman and Roy | The Bridge: From research To PracTice The Peer Tutoring Literacy Program™: Achieving Reading Fluency and Developing Self-esteem in Elementary Scho
Bibliographic record
Abstract
Editor’s note: The evaluation of the Peer Tutoring Literacy ProgramTM can be read on p. 9 in this issue of the ACIE Newsletter. The Peer Tutoring Literacy ProgramTM materials are available to individual members of Canadian Parents for French (CPF) free of charge (except for cost recovery of DVD and mailing). Schools that implement the program must also be associate members of CPF. For more information, see www.cpf.ca/English/ FAQ. The advent of the knowledge economy and its ever-increasing reliance on the Internet and other media platforms has placed greater weight on attaining varied and sophisticated literacy skills. Nonetheless, achieving a firm foundation in basic literacy remains a challenge for many primary students, an issue that is even more problematic in immersion education. In 1998, Nicole Roy, a resource teacher at Lord Tennyson Elementary, a French immersion school in Vancouver, Canada, already had established a successful drop-in literacy program run by parent vol-unteers, including Mary Chipman, to prepare Grade 3 students for the introduction of English-language curriculum in Grade 4. In our dis-tinct capacities as a parent volunteer and a resource teacher, we were also involved with a research project run by the British Columbia Teachers’ Federation (BCTF) called Exploring
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.098 | 0.026 |
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".