Supporting Authors During the Writing Process: JME’s Online Manuscript Development Workshops
Bibliographic record
Abstract
The Journal of Management Education has a long history of supporting authors at all stages of the research and writing process, including "Meet the Editor" sessions (both online and in person), abstract reviews by email, and Editors' Office Hours.These different options provide interested authors with an overview of our aims and scope, offer tailored early feedback on a manuscript's potential fit for the journal, and create an opportunity for open dialog and advice at any stage of writing.Based on the Management & Organizational Behavior Teaching Society's (MOBTS, n.d.; the sponsor of the journal) commitment to diversity, equity, inclusion, and belonging and its long-standing desire to further internationalize JME's authors and readership, the editorial team felt it was critical to offer more substantive feedback opportunities to authors outside of face-to-face conference settings.While we enjoy meeting people in person, traveling to conferences, for many, continues to be challenging due to such factors as costs, visas, and caretaking roles.Over the past two years, we have hosted online manuscript development workshops (MDWs), also known as paper development workshops, with our Editorial team and authors.
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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 | 0.070 | 0.553 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.241 | 0.085 |
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".