Keeping it Real: Why Case Research Writing Conventions Need to Loosen Up
Bibliographic record
Abstract
The case method remains the signature pedagogy in management education. Teaching case research is a form of qualitative research which has recently gained enhanced recognition by accreditation bodies as scholarly or intellectual contributions. As proponents of the case method, however, we are concerned that in the peer-review process case researchers struggle to publish cases that honor the desired learning outcomes and fully adhere to the standards of research trustworthiness. We believe this happens because several common case writing conventions of mainstream case publishing are impeding the publication of pedagogically valuable cases. The purpose of this essay is to describe and raise awareness about the conflict between accurately representing case data and these established conventions. Accordingly, we describe how the mainstream case publishing process works, including the case conventions researchers must follow, and explain how the strict adherence to these conventions can conflict with the trustworthiness of case data, as well as with pedagogical objectives. We conclude by suggesting that the case publishing community should allow more flexibility in how cases are written in recognition of their scholarly purpose, and we provide concrete suggestions for how to accomplish this cultural shift. Our aim is to keep case research real.
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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 | 0.589 | 0.754 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.021 | 0.120 |
| Scholarly communication | 0.046 | 0.057 |
| Open science | 0.013 | 0.024 |
| Research integrity | 0.020 | 0.034 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".