Bibliographic record
Abstract
The Journal of Teaching and Learning focuses on issues of teaching and learning – obviously. However, teaching and learning are anything but straightforward, in all their many permutations and implications in school and in life. In this issue, each article approaches teaching and/or learning from a different vantage point: from the view of teachers, of teacher candidates, of school students, of administrators, ministries of education, and curriculum designers, in Canada, Africa, and Asia. Despite the apparent diversity of this collection, the articles contain many areas of overlap and points of convergence. While taking the Journal of Teaching and Learning online, and providing open access has been a great boon to our ever-increasing readership, I wonder, “What are we missing?” What has been lost, from the days when we sat down with a hard copy in our hands, started by scanning the table of contents, and then dove in, reading articles willy-nilly, in or out of order? I encourage you to read more than one of the articles in this issue, and to read ‘across’ for meanings that can be found between texts.
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 | 0.012 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.024 | 0.019 |
| Insufficient payload (model declined to judge) | 0.061 | 0.041 |
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".