Bibliographic record
Abstract
This next Canadian Journal of Learning and Technology issue is published on the heels of the well-attended ICDE (International Council for Open and Distance Education) conference. The Conference’s overlapping topics and attendant researchers, well-known to this journal, remind us that our field is important, well-subscribed, growing, and changing. An excellent overview of this ICDE Conference and information about the state of education transformation in the current global context can be found here in recent blog posts by the well-known expert and author on the topic of education and technology, Dr. Tony Bates. Learning and technology, the focus of research published by CJLT, is a microcosm in the much larger fields of open, distance, and digital education. Research spans all sectors: primary, secondary, post-secondary, higher education, and lifelong learning. Across issues and years, we seek to touch on the research, theory, and practice in these areas, particularly those where authors are in, or research topics are relevant to, Canada. Canadian researchers were well-represented at the recent ICDE conference, and a Canadian researcher received the conference’s best paper award!
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.102 | 0.058 |
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".