Bibliographic record
Abstract
t is an honour and a privilege to have been invited to become the Editor of the new, online, open access, and peer-reviewed journal entitled LEARNing Landscapes.I was pleased to accept this position because I believe the collaboration between the Leading English Education and Resource Network (LEARN) 1 and McGill in this project will provide an important link between the University and the educational community in Quebec, and beyond.Also, I believe the work will provide the opportunity to blur boundaries between theory and practice, and between discipline and research divides, and across educational sectors, areas that are striving for similar goals, but with little time to entertain exchanges or collaborative activities.The added appeal of the work is that the journal will be available to anyone and everyone without a subscription, and the technology will allow imaginative experimentation with different kinds of texts and graphic possibilities.Less than a year ago, during a meeting with Michael Canuel, CEO of LEARN and his colleagues Rosa Kovalski and Mary Stewart, our discussion wandered off the immediate topic and we ended up in a wonderful "what if" moment.It was then that the kernel idea for LEARNing Landscapes was born.Within a month plans were solidified, a trajectory was set and the journey began.It has been an exhilarating one.I am totally indebted to Michael Canuel for his openness and support, to Mary Stewart, Managing Editor, for her friendship, conscientiousness and organizational skills, to Maryse Boutin, Graphic Designer, for her talent and patience, to David Mitchell,
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.063 | 0.053 |
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".