Ontario Association of Physics Teachers1
Bibliographic record
Abstract
Sharing is Caring – for Yourself and Your Students Welcome to another fabulous year of physics teaching. In this issue two of our PER experts give their next installments in nudging us towards more enlightened practice, and we learn about the Diffraction Dance! As a foil to all this pedagogy I present my first self-authored article for the Newsletter since becoming editor. I say my article is a foil because I merely tell a story. I hoped to convey that any member who wishes to contribute to the Newsletter need not be touting cutting edge pedagogy or revealing arcane physics. Whether you teach physics in the backwoods of Ontario’s northland, in the inner city of Ontario’s capital, or anywhere in between, your experiences are unique, but they likely have many commonalities with those of your physics colleagues across the province. For both of these reasons your stories are of value. Share them. Take the risk. We want to know what’s really going on ‘out there ’ in the classrooms. Where do you teach? How do you teach? Why do you teach? What makes physics teaching interesting or unique for you
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.281 | 0.082 |
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".