Diversity or Identity Crisis? An Examination of Leading IS Journals
Bibliographic record
Abstract
Since its founding in the 1960s, the Information Systems (IS) field has been involved in critical debates about the nature and future of the discipline. Many researchers feel that diversity in IS research is our strength; others fear that too much diversity leads to losing the field's core identity. Do the scholarly contributions of the IS community reveal either of these two phenomena? In order to address this question, we examine articles published in leading IS journals (MISQ, ISR, and JMIS) during the period of 2000 to 2006. Our analysis includes classifying the articles using a classification scheme that includes the consideration of IT artifact, the research methods used, and the research topics covered. We provide descriptive statistics following a content analysis procedure and results based on cluster analysis and association rule mining. Our results provide an update on previous findings on IT artifact and its consideration in IS publications. Our results further suggest that while our leading journals cover a broad range of research topics and methods, there is also evidence of popularity on some topics and research methods.
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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 | 0.018 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.043 | 0.067 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".