Research at the Centre for Educational Research on Languages and Literacies (CERLL) at the Ontario Institute for Studies in Education of the University of Toronto (OISE/UT)
Bibliographic record
Abstract
After more than 40 years as the Modern Language Centre, members of the Centre decided to rename ourselves as the Centre for Educational Research on Languages and Literacies (CERLL), to better reflect our current activities and interests. We officially launched the new name for the Centre at a reception on 22 October 2010, and produced a compilation of recent publications by members of the Centre to mark the event. Our interests in research and graduate studies remain fundamentally as they have been for decades, focused on theories and practices in teaching, learning, curriculum, assessment, and policies related to English and French as second or international languages as well as other international, minority, heritage, or indigenous languages. The name change does signal a broadening of perspectives to include research on various forms and types of literacies, though we do not claim to be ‘post-modern’ in doing so.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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".