Bibliographic record
Abstract
One goal of weather and climate forecasting is to inform decision making. Effective communication of forecasts to various sectors of the public is essential for meeting that goal, yet studies repeatedly show that forecasts are not well understood by lay people. Using a case study from northeast Brazil, this article discusses some of the communication difficulties faced by forecasters and outlines an approach for adapting forecast language to users' needs and expectations. Analysis is based on data collected during 14 months of fieldwork, including interviews, a survey, and observations of meteorologists and local “rain prophets,” whose predictions are derived from empirical observations. The anthropological approach emphasizes the importance of language. For example, findings indicate that forecast communicators should look for multiple definitions of key terms that have common as well as technical meanings. Distinctions salient to meteorologists may be meaningless to the public, even when terms are clearly defined. In some cases, it maybe more helpful to work with lay concepts when communicating forecasts rather than dismissing such understandings as “incorrect.” Meteorologists should also recognize that scientific concepts are not accepted by everyone as the only correct way to think. This is especially relevant where scientific forecasts are competing with alternatives, such as those based on traditional knowledge. Finally, forecast communicators should develop the format and content of the forecast within each application. It is important to learn what people expect from forecasts and which communication styles are preferred.
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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 | 0.011 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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".