Conceptualizing Confidence: A Multisited Qualitative Analysis in a Severe Weather Context
Bibliographic record
Abstract
Abstract Confidence is a concept important to weather prediction, shaping how risk information is created, shared, understood, and acted upon. For forecasters in the National Weather Service (NWS) and their partners in public safety, confidence is central to their work, appearing frequently during their decision support services. While confidence has been examined in a variety of literatures, it is often addressed simplistically or as one of many variables in a study. It is rarely the object of study in and of itself, even less so in a naturalistic setting like an operational environment. To build a more robust knowledge of confidence and its many dimensions, we conducted a multisited ethnography of three interrelated sites central to tornado prediction and information dissemination, leading up to and during a cool-season tornado event. In partnership with collaborators from the NWS and emergency management, we simultaneously deployed to a National Center, a local Weather Forecast Office, and an emergency management office. This article explicates confidence from multiple social science theories, considering the scientific, data-based roots of confidence, as well as its affective, relational, and procedural origins. Our results show that confidence emerges in varied and complex ways and at different scales. Confidence can indicate one’s assessment of evidence and agreement (or lack) of it, beliefs about partners’ future behavior based on past experiences, and ritual interactions between offices that create patterned expectations. We argue for a more robust interdisciplinary analysis of confidence given how it shapes weather-related policies and practices, technologies, and communication strategies.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".