The Ends of Information Systems (is) Research
Bibliographic record
Abstract
In this paper, we argue that any effort to understand the state of the Information Systems field has to view IS research as a series of normative choices and value judgments about the ends of research. To assist a systematic questioning of the various ends of IS research, we propose a pragmatic framework that explores the choices IS researchers make around theories and methodologies, ethical methods of conduct, desirable outcomes, and the long-term impact of the research beyond a single site and topic area. We illustrate our framework by considering and questioning the explicit and implicit choices of topics, design and execution, and the representation of knowledge in experimental research—research often considered to be largely beyond value judgments and power relations. We conclude with the implications of our pragmatic framework by proposing practical questions for all IS researchers to consider in making choices about relevant topics, design and execution, and textual representation of findings in their research.
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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 | 0.178 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.011 | 0.109 |
| Scholarly communication | 0.038 | 0.062 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".