Bibliographic record
Abstract
This issue of The International Review of Research in Open and Distance Learning is memorable for two reasons.The first is that this is the largest issue (23 research articles and a book review) in the 13 years of IRRODL publication.We assume this increase is directly related to the growing reputation of IRRODL as being a journal that is widely read (averaging 84,000 full text downloads/month), cited (55% of articles are cited by authors in other Scopus indexed journals and an SSCI impact factor of .69 in 2011), and indexed by all the major citation indexing systems.In addition authors have come to expect timely and helpful reviews.We have been able to review, edit, and publish in efficient and timely fashion (averaging less than 6 months from submission to publication) with acceptance rates slightly less than 50%, and to my knowledge no other journal publishes in the four different formats in which we distribute each article.None of these accomplishments could have been attained without the efforts of a network of very talented and hardworking volunteers reviewers and editors.But journal production of the magnitude of IRRODL today could also not have happened without the leadership of our founding editor, Dr. Peter Cookson, nor without the full time work of our managing editors, first Jan Thiessen, then Paula Smith, and for the past five years Brigette McConkey.Finally IRRODL could not be distributed freely,
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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.033 | 0.025 |
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".