Bibliographic record
Abstract
Transitions in LearningWhile some aspects of moving a journal from print to online format are relatively easy, other things are more challenging.The delayed publication of this issue (normally published in October/November) resulted from a temporary drop in submissions to the CJSAE/RCÉÉA that followed our switch to online.Thankfully, in the past few months, submissions have picked up dramatically leaving us with the happy prospect of having a number of exciting articles reviewed and scheduled for publication in our spring issue and a number of other manuscripts in the process of being reviewed.Moreover, we are in the final stages of editing and formatting a "special issue" of the journal, due for publication mid-March.Guest edited by Colleen Kawalilak and Janet Groen of the University of Calgary, this issue will explore the place of adult education in Faculties of Education in Canada.We anticipate that this special issue will attract a global readership of academic adult educators concerned about the place of adult education studies in the contemporary university.With our journal collection since its inception now accessible online, we are sure that authors and readers will discover/rediscover the Canadian Journal for the Study of Adult Education/La Revue Canadienne Pour L'étude de l'éducation des adultes (CJSAE/RCÉÉA) as a premier publication in the field of adult education.
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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.029 | 0.028 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".