Bibliographic record
Abstract
As Journal of Management Education (JME) editors, the critically important work of reviewing is never far from our minds. During our post–Academy of Management meeting debrief conversations, it again has taken a forefront position. Each of us heard more than one Program Chair in Vancouver lament how difficult it continues to be to get solid reviews; one divisional Chair noted that, although submissions had increased about 30%, the reviewer corps had slightly decreased in numbers despite repeated entreaties to that division’s community. Our editorial team, too, has noted increasing difficulty in wrangling the terrific reviews that JME authors have enjoyed for 40 years. We think it is important to address what might be going on throughout our discipline and perhaps elsewhere in academe as well. As we move through the various tasks of our work as editors, particularly when representing JME at conferences, a significant portion of what we do is devoted to the reviewing process—asking and encouraging individuals to volunteer as reviewers, talking about how important it is for JME to offer developmental reviews, conducting workshops on how to craft such reviews, and counseling authors on the ways to consider reviewer feedback as a basis for improving their manuscript. All of our engagements with reviewers and authors remind us that reviewing is an essential contribution to knowledge creation in our field, a fundamental activity that helps maintain the high
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.044 | 0.020 |
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".