The Identity Crisis Within the IS Discipline: Defining and Communicating the Discipline’s Core Properties1
Bibliographic record
Abstract
We are concerned that the IS research community is making the discipline’s central identity ambiguous by, all too frequently, under-investigating phenomena intimately associated with IT-based systems and over-investigating phenomena distantly associated with IT-based systems. In this commentary, we begin by discussing why establishing an identity for the IS field is important. We then describe what such an identity may look like by proposing a core set of properties, i.e., concepts and phenomena, that define the IS field. Next, we discuss research by IS scholars that either fails to address this core set of properties (labeled as error of exclusion) or that addresses concepts/phenomena falling outside this core set (labeled as error of inclusion). We conclude by offering suggestions for redirecting IS scholarship toward the concepts and phenomena that we argue define the core of the 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 | 0.066 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.022 | 0.103 |
| Scholarly communication | 0.029 | 0.036 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.022 | 0.030 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".