Bibliographic record
Abstract
It is an honour to share selected reflections on my eight years as editor of the Alberta Journal of Educational Research (AJER).When I took on the role of editor, I could not have imagined the changes that have occurred in that time, and some associated challenges.Over the eight-year period AJER has undergone substantial changes.Major amongst these were the move from being a paper-based journal to an online journal, the dissolution of the Editorial Advisory Board and its replacement with a fully populated Editorial Review Board, and the move to engage graduate students in the production process of the journal as copyeditors.When I began as editor the move to an online format was already well underway through the leadership of then editor, Professor Larry Prochner.Along with this move to 'online' has been a more recent shift of the moving subscriber access wall from two years to one, to reflect changes in the publishing responsibilities of authors publishing from their Social Sciences and Humanities Research Council (SSHRC) funded research.The transition was remarkably smooth and, in general, these moves have resulted in positive outcomes for the journal.These include an increasingly wide readership, an increase in the number of submissions (from an increasingly wide range of authors and countries), and an increased efficiency in submission, review, and production processes.Through these shifts, AJER has maintained its reputation as a high quality, eclectic, and affordable journal.These have been positives for the journal.However, of course, there have been challenges.I wrote in Vol.57, No. 1, Spring 2011 (Thomas, 2011) that "recent times have witnessed a marked increase in the number of journals in educational research."This trend has continued at a rapid pace.The majority of new journals are online.Some attend to fields of education that are emerging, or fields where authors are seeking to establish very focused outlets for their scholarship.Other journals attend to specific regional orientations.Most are bona fide, seeking like AJER to publish "original, high-quality research and scholarship in the field of education that has been subject to stringent peer-review" (Thomas, 2011).However, there has also been a marked rise in what have become known as 'Predatory Journals.'These journals are often characterized by limited, if any, qualified peerreview processes, unimaginably fast review and publication times, and, most notably, author fees to publish.Let me be clear that these three factors most often 'cluster' together in the case of such journals.There are of course bona fide journals that charge authors publication fees, and this is not a new practice.What characterizes the predatory journal is the general lack of quality peer review, and the very short times from submission to publication.Most established and experienced authors can (a) recognize such journals, (b) know to steer clear of their requests for submissions and the inevitable promise of speedy publication, and (c) provide advice to those in their communities including emerging scholars to be careful about publishing options.However, the emergence and increasingly visibility and influence of such journals has occurred because of the pressure exerted on many if not most academics to 'publish or perish' and the often-enticing
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 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.034 | 0.033 |
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".