Virtual Trust in High‐Reliability Organizations
Bibliographic record
Abstract
ABSTRACT Although much is known about trust in organizations, questions remain on how trusting relationships may develop in high‐reliability organizations that depend on information technology in their routine operations. Because of their dispersed operations, operational staff of high‐reliability organizations such as the armed forces in this article may often only interact virtually. Based on military field operators' accounts of a “near‐miss incident” at a military airbase in Afghanistan, the article explores how organizational trust can be activated in a high‐reliability organization during an armed conflict by drawing on actors' use of a non‐face‐to‐face form of “virtual” trust. In the article's case study of virtual trust, field operators with no prior interaction jointly took preventive action to head‐off a potentially catastrophic incident without authorization from their manager. We theorize that operators acted successfully by maintaining a state of constant operational readiness in an environment of virtual trust. A framework of operational readiness is presented for high‐reliability and other organizations whose activities risk significant public harm. Implications of virtual trust are presented for practitioners and researchers in enabling frontline operators to act beyond hierarchical constraints for the preservation of the organization and its environment.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".