What is a Contribution to IS Design Science Knowledge
Bibliographic record
Abstract
In order to promote more rigor in Design Science Research (DSR), Gregor and Hevner have proposed guidelines for conducting and evaluating DSR in the Information Systems (IS) discipline. Their work has been influential and widely used to advance the field. However, the way they characterize IS-DSR knowledge contributions excludes what we believe are genuine contributions and includes works that in our lights are not contributing to IS-DSR knowledge. To overcome this problem, we borrow from the contemporary philosophy of science to develop a framework for identifying the types of IS-DSR knowledge. We posit that contributions to DSR are in the form of theories or technological designs, and each type could be either inter-field or field. We demonstrate the strength of the proposed framework in better identifying contributions and clarifying the boundaries of IS-DSR. Our experience led us to believe that the proposed view is applicable to the whole IS discipline.
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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 | 0.082 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.034 | 0.046 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".