Bibliographic record
Abstract
Welcome to Volume 50, Issue 3, of The Canadian Journal of Learning and Technology (CJLT). CJLT is a peer-reviewed journal that has supported research and practice in technology for learning for decades. This bilingual journal is free of charge to anyone with Internet access, is multi-indexed, and is presented in accessible formats. There are no article submission or publication fees. Amid threats of a trade war and an imminent federal election in Canada, the editorial team completed and released this issue. Discussions about the decline of civil society and the needs of democracies surround us. We are reminded that education plays a crucial role in fostering informed, engaged, and responsible citizens, which is essential for civil discourse and productive civic participation. More broadly, Canadian and global education remains focused on the development needs of individuals and the socioeconomic world. These needs are shaped by the increased use of artificial intelligence tools, as well as the impact of remote learning and learning losses experienced during the COVID-19 pandemic. CJLT continues to provide research addressing education in this evolving landscape of contextual and technological change.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.195 | 0.121 |
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".