Bibliographic record
Abstract
The present issue of CJLT includes eight articles (six research papers, two position papers) that explore diverse areas of educational technology research and a book review.The fourteen authors who have contributed their research and critical perspectives to this issue hail from universities across Canada and the United States.The following section provides an overview of the research and commentary presented in this issue.Elizabeth Murphy, from Memorial University Newfoundland, provides the first article, entitled "A Framework for Identifying and Promoting Metacognitive Knowledge and Control in Online Discussants".In this position paper, Murphy develops a framework to be used by researchers for analysing transcripts of online discussions for evidence of engagement in metacognition, by instructors assessing learners' participation in online discussions or by designers setting up metacognitive experiences for learners in online settings.Murphy's framework improves upon existing models or frameworks (e.g., Henri, 1992) that support the identification and assessment of metacognition which have been described as subjective, lacking in clear criteria, and unreliable in contexts of scoring.
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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.043 | 0.029 |
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".