On the Foundations of NeuroIS: Reflections on the Gmunden Retreat 2009
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it