Bibliographic record
Abstract
EditOriALWe would like to open this issue, which marks nearly the end of the second year of our tenure as MJE co-editors, and of the second complete MJE volume that we've shepherded through the publication process, by thanking our reviewers.Over the journal's almost fifty years, our reviewers have come to number almost two thousand (as we learned through a recent count).It is not the faceless two thousand that we wish to acknowledge here, but the individuals behind that daunting number: the ones who, over the course of the last two years, we, as 'green' editors, have relied upon to give of their time in providing appraisals of pieces in various states of being ready (or not yet) for publication.These individuals are academics (established and emerging) who work or conduct research at various universities, English and French, across North America and around the world.As newcomers to journal editing, we hadn't given a great deal of thought to reviewing; it was something that we understood was important to do as academics but that we very much took for granted.In two years, we have learned a great deal from our reviewers, lessons that have no doubt informed our own practices as reviewers for other journals, including "being good" by respecting deadlines: a small but crucial part of the review / publication process.As Anthony Paré, previous editor of the MJE, has remarked (45, 1), the amount of time most reviewers give to thoughtful and often long and detailed reviews is chastening, especially when we consider that reviewers perform this service for a given piece not once but frequently twice, and when we also consider that the value ascribed to academic reviewing (a cornerstone of the publication process, on which we all rely) is rapidly being eroded in favour of other so-called priorities; where even reviewing has become the focus of "cherry-picking:" of being implicitly or explicitly encouraged to undertake only those guaranteed to stand out on a CV or for the purposes of securing merit.And yet we would not be able to exist as a peer-reviewed, open access journal-a common space for scholarly dialogue and exchange on issues important to education locally, nationally and internationally-without that "essential service."All the more reason to take the time to heartfully say: thank you, reviewers!We at the MJE appreciate the work that you have done for all of us, and hope to be able to call on you again in the future.All the best for the new year!
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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 | 0.006 | 0.040 |
| 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 | 0.009 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.207 | 0.144 |
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".