Bibliographic record
Abstract
We are, at last, able to publish the third issue of 2019 of CJLT. As with many things, the COVID-19 epidemic and the required response demanded much attention and activity. The resulting closure of schools and post-secondary education institutions created a draw on many of us involved in education delivery. Far from carefully designed online and blended teaching and learning, emergency remote teaching and learning did benefit from the support and direction from many in our field of learning with technology. We welcome those moving online as fellow colleagues; faculty, lecturers, instructors, instructional designers, and administrators in postsecondary and higher education and teachers and administrators in the K-12 system. We look forward to learning from the documentation and research about these new and unique experiences of educators. These outcomes of the Covid-19 experience, outcomes of alternative forms of education delivery in the emergent move to remote or distance delivery will be welcome. They will guide other educators in any situation where distance, online, technology-enabled and/or digital education delivery is needed or desired. We wish all involved the very best in this endeavour.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".