INTRODUCTION TO THE SPECIAL ISSUE ON NOVEL PERSPECTIVES ON TRUST IN INFORMATION SYSTEMS
Bibliographic record
Abstract
Research on trust has taken center stage in the MIS field in the past few decades, covering a wide range of trust-related topics based on a multitude of theories from sociology and psychology to economics. To extend this rapidly emerging trend and identify some ground-breaking perspectives on the study of trust, this special issue of the MIS Quarterly on “Novel Perspectives on Trust in Information Systems” aims to explore novel aspects of trust in new and under-researched IS contexts. In brief, the intent of the special issue was to publish innovative research articles about (1) novel antecedents of trust, (2) the construct of distrust and its relationship to trust, (3) the boundaries of trust, and (4) the study of trust in new and unexplored MIS contexts (Benbasat et al. 2008). The papers submitted were first screened by the editors, in some cases aided by an associate editor, to verify their appropriateness to the topic of the special issue and their novelty. The remaining manuscripts went through the MIS Quarterly’s standard, rigorous review process, including the usual “arms-length” and “conflict of interest” guidelines for the senior editors, associate editors, and reviewers in the handling of the papers. Interestingly, neither of the papers eventually accepted utilized traditional research methods commonly used in past research on trust in MIS. Indeed, the two papers that appear in this special issue dealt with what to MIS research are rather novel methodologies (namely functional brain imaging, specifically functional magnetic resonance imaging, or fMRI) (see Belliveau et al. 1991; Friston et al. 1994; Logothetis et al. 2001; Ogawa et al. 1990), which enabled these two papers to offer new insights into topics that were out of reach for the more traditional research methods previously used in trust research. By no means do we imply, however, that future novel contributions to the study of trust in MIS research should be limited to any particular research methodologies.
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How this classification was reachedexpand
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".