Writing IS Teaching Cases: Guidelines for JISE Submission
Bibliographic record
Abstract
Over the past year, JISE began accepting Teaching Cases for publication. Many information systems (IS) academicians and professionals have valuable experiences that are ripe for translating into cases. Students and instructors would benefit from the communication of these experiences into realistic, well-written cases to promote active learning. To encourage case submissions, the purpose of this article is twofold: (1) to provide potential authors with helpful advice and suggestions for writing case manuscripts; and (2) to communicate JISE guidelines for Teaching Case submissions. This paper is designed to serve as a resource for writers who are interested in developing Teaching Cases for publication.
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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 | 0.149 | 0.488 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.020 | 0.045 |
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".