A Retrospective Examination of a Successful Developmental Reviewing Process
Bibliographic record
Abstract
In this essay, we describe a retrospective examination of a review process for a manuscript that was published in the Journal of Management Studies (JMS) in 2015 (Hannah &Robertson, 2015). The two authors of the essay are (a) the first author of the JMS manuscript, David Hannah, and (2)the JMS editor of that manuscript, Dries Faems. We originally engaged in this examination to prepare for a presentation at the Strategy Process Interest Group workshop on“The Process of Publishing Process Research” during the 2015 Strategic Management Society meeting in Denver, Colorado. We have written this essay with a goal of sharing our observations about this review process. Although we share some of the content of the original manuscript herein, we focus most of our attention on describing each step in the review process from the perspective of the author as well as the editor. We conclude by offering what we hope are usefuland generalizable lessons about the challenges that authors and editors face in the review process, how to navigate them, and how to systematically improve the overall review process.We begin in July 2013, with JMS submission P0431, titled, “Why do Employees put Confidential Information at Risk? CI Protection and Confidentiality Tension in High-Tech Employees.” The paper reported the findings of a qualitative, theory-elaborating study involving 55 semistructured interviews with the employees of two high-tech companies.
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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 | 0.203 | 0.604 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".