NeuroIS: Challenges and solutions
Bibliographic record
Abstract
Consistent with the ICIS conference theme “Gateway to the Future,” this panel will debate the advantages of pursuing NeuroIS – an emerging area in the IS discipline that offers a new lens into IS phenomena by looking into the brain’s functionality – relative to the challenges inherent in adopting a new set of neuroscience theories and tools . The panelists will debate whether the difficulties involved in conducting NeuroIS studies outweigh their benefits, and whether it is possible to overcome these challenges. Izak Benbasat will outline the process of conducting NeuroIS studies, including identifying interesting IS research problems, designing experiments, and presenting results. Kai Lim and Eric Walden will focus on the challenges of NeuroIS studies, while Angelika Dimoka will seek to counteract these challenges with a set of solutions. From an editor’s perspective, Detmar Straub will discuss the challenges in editing and reviewing manuscripts that rely on novel (neuroscience) theories and (neurophysiological) tools, offering guidelines for authors for publishing in this new area. The panel seeks to have a broad appeal to IS researchers who may be interested in NeuroIS but may be impeded by its challenges. The panel’s ultimate goal is to assess if these challenges could be overcome and give IS researchers a set of actionable solutions to conduct high-quality studies.
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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 | 0.082 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.023 | 0.033 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.020 | 0.020 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".