Avoiding Speedbumps at the Start of the Review Process
Bibliographic record
Abstract
As editors, we want nothing more than to put impactful management education-related manuscripts into the hands of our dedicated Associate Editor team.Upon submission, however, small speedbumps-such as formatting issues or changes required to remove potentially identifying informationcan delay our ability to move manuscripts that are within the journal's aims and scope to the review process.Often, management education research articles, essays, and instructional innovations are grounded in the authors' experience in their classrooms and institutions.As such, it can be challenging to present a sufficient amount of contextual information for readers, while avoiding potentially identifying details.We very much appreciate that authors are eager to share this information, in order to be transparent about their experiences and processes.However, in an era of efficient internet search engines, the way the information is presented can occasionally offer clues to the institutions that the authors are affiliated with.With this in mind, we decided to focus our second editorial of 2025 on advice for how authors can navigate this, thus avoiding potential delays upon their initial submission to the journal.We condense this advice into Table 1-though it does not include an exhaustive list of potential speedbumps, we hope that it does provide useful tips.
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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 | 0.351 | 0.758 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.028 | 0.024 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.016 | 0.030 |
| Insufficient payload (model declined to judge) | 0.025 | 0.048 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".