Advancing a NeuroIS research agenda with four areas of societal contributions
Bibliographic record
Abstract
On the 10th anniversary of the NeuroIS field, we reflect on accomplishments but, more importantly, on the future of the field. This commentary presents our thoughts on a future NeuroIS research agenda with the potential for high impact societal contributions. Four key areas for future information systems (IS) research are: (1) IS design, (2) IS use, (3) emotion research, and (4) neuro-adaptive systems. We reflect on the challenges of each area and provide specific research questions that serve as important directions for advancing the NeuroIS field. The research agenda supports fellow researchers in planning, conducting, publishing, and reviewing high impact studies that leverage the potential of neuroscience knowledge and tools to further information systems 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 | 0.070 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.021 | 0.037 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.027 | 0.037 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".