Bibliographic record
Abstract
I began using blended learning four years ago as a secondary school teacher in Ontario. I started small, using one feature at a time, but quickly became hooked. The more I learned how to do, the more I wanted to explore and try new things. This learning and growth gave my teaching a renewed energy because I was excited about new things that I was now able to do, and for the first time in my adult life, I was excited about my own learning. My love of blended learning and new-found interest in technology soon led to seeking other ways to integrate technology into my classroom. At this point, there was no turning back. Last year I moved out of the classroom and into a different position, as an Instructional Coach. My job is to support teachers at my high school in adopting new practices and trying new approaches, so much of what I do is related to technology. Both as a classroom teacher and in my new role I’ve seen colleagues begin a semester by setting up a course in our Learning Management System (LMS), but for a variety of reasons, some of them don’t end up using blended learning as the semester progresses. Why does this initial interest not always mean that blended learning is adopted?
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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.023 |
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".