Innovations in Teacher Development for the Knowledge Age
Bibliographic record
Abstract
Lamon is a senior research scientist with the Institute for Knowledge Innovation and Technology (IKIT) and the Ontario Institute for Studies in Education of the University of Toronto. She has been with IKIT since 1996 where she was involved in the Canadian Telelearning National Centres of Excellence program designed to research information and communications technology development in K-12 education. Previously, Lamon directed the international Schools for Thought program, that integrated cognitive research from OISE/UT, Vanderbilt University and the University of California at Berkeley. \n \nLamon received a PhD in experimental cognitive psychology from the University of Toronto. Her research as a McDonnell post-doctoral fellow led to research in elementary classrooms where students in CSILE classrooms were encouraged to become self-directed, intentional and reflective learners. Lamon is currently researching the evolution of knowledge building communities, the development of teacher expertise, and on how knowledge building & the creation of improvable artifacts increase literacy as a by-product. She is also working on Scardamalia's “Beyond Best Practice” initiative designed to study and promote innovation across sectors, ages and countries.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".