End‐user satisfaction with Hurricane Dorian information in Atlantic Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Both Environment and Climate Change Canada (ECCC) and the National Oceanic and Atmospheric Administration have focused significant time and resources towards improving their forecast products. However, weather prediction remains an imperfect science, and as such, it is not unusual for meteorologists to prioritize accuracy over consistency or vice versa. There is considerable debate within the literature about whether (and how) inaccuracies and/or inconsistencies in forecasting will affect end‐user trust in future warnings. Hurricane Dorian presented the opportunity to explore the intersection between these concepts as its messaging was at times both inaccurate (e.g., then‐President Donald J. Trump indicated the storm would directly affect the state of Alabama) and inconsistent (i.e., both the storm's forecasted intensity and track changed over time). Two research projects were undertaken in Atlantic Canada: the first utilized semi‐structured interviews to examine the ways that ECCC meteorologists ( n = 6) perceived the needs of their end‐users during the storm. There was considerable concern that changes in the storm's forecasted track and intensity would negatively influence public response. The second project utilized a large sample questionnaire ( n = 1218) to examine ways that end‐users searched for, shared, and responded to storm‐related information. Despite changes in the storm's track and intensity as it approached Atlantic Canada, as well as the international news coverage of Sharpiegate, respondents overwhelmingly agreed that the storm was well forecasted and its impacts were well predicted. The implications for this (seemingly) contradictory response are explored in the context of probabilistic forecast potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.012 | 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 it