ICIS Panel Summary: Should Institutional Trust Matter in Information Systems Research?
Bibliographic record
Abstract
This paper summarizes and expands the panel on "Should Institutional Trust Matter in Information Systems Research?" that was presented during the ICIS 2005 Conference in Las Vegas. The panel was co-chaired by Paul A. Pavlou of the University of California and by David Gefen of Drexel University. The panelists were Izak Benbasat of the University of British Columbia, Harrison McKnight of Michigan State University, Katherine Stewart of the University of Maryland, and Detmar W. Straub of Georgia State University. There were about 150 people attending the panel and taking part in the lively discussion that pursued. Due to the interest the panel aroused, this paper expands on the topics discussed and presents them in a much broader perspective in a set of appendices.
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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 | 0.041 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.024 | 0.019 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".