On the Foundations of NeuroIS: Reflections on the Gmunden Retreat 2009
Bibliographic record
Abstract
This article reflects on the discussions of the fifteen participants (co-authors) of a retreat on the “Foundations of NeuroIS” that took place in Gmunden (Austria) in September 2009. In particular, this article offers initial answers to a set of research questions which are important for the foundations of NeuroIS, an emerging subfield within the IS discipline. The key questions discussed during the retreat that are addressed in this article are: (1) What is NeuroIS, and how does it relate to sister disciplines, such as neuroscience, neuroeconomics, and neuromarketing? (2) Which neuroscience tools are relevant for IS research? (3) What can IS researchers learn from the neuroscience literature, and what do we already know about brain activity? (4) What are possible IS research topics that can be examined with neuroscience tools, and what are some promising research areas for NeuroIS? (5) How can NeuroIS be established as a new subfield in the IS literature, and what are the current challenges for NeuroIS? The article concludes by offering the participants’ outlook on the future of NeuroIS.
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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 | 0.060 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.026 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.026 | 0.046 |
| 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".