Institutions and Trust: Crisis, Erosion, and Construction
Bibliographic record
Abstract
Management and organization scholars have increasingly directed their attention to the study of grand challenges such as socioeconomic inequality, job losses, climate change, and the resurgence of populism. A core aspect of these interrelated challenges concerns an accelerated decline of trust in core societal institutions such as science, the law, and democracy itself. Research on trust or institutions has a long history in the field of management and organization studies. However, relatively few scholars have adopted an institutional approach to trust. To understand trust as a complex, macro-level phenomenon, we propose a research agenda that focuses on studying the dynamic relationship between institutions and trust. This symposium includes four papers that explore important aspects of these dynamics—including how global crises endanger the perceived trustworthiness of social institutions, how the erosion of trust in institutions unfolds, how institutionalization affects the level of trust, how organizations strategically use institutions to address the loss of trust, and how they use digital technologies as a governance institution to construct trust. Our agenda opens up new opportunities for research on the co-constitution of trust and institutions. Institutional trust in the age of global crisis Author: Maxim Voronov; Schulich School of Business Author: Gerardo Patriotta; U. of Bath The erosion of trust in modern institutions: The argumentative structures of science denial Author: Renate Elisabeth Meyer; WU Vienna & Copenhagen Business School Author: Dennis Clemens Jancsary; WU Vienna Trust of and in organizations: An institutional account Author: Oliver S. Schilke; U. of Arizona Author: Fabrice Lumineau; U. of Hong Kong Firm digitalization, trust, and trade credit receiving Author: Daokang Luo; Department of Management and Strategy, The U. of Hong Kong Author: Kevin Zheng Zhou; U. of Hong Kong
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".