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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it