Making Forecasts Meaningful: Explanations of Problematic Predictions in Northeast Brazil
Bibliographic record
Abstract
Abstract This study illustrates the need to consider the multiple interpretations and experiences that influence how climate forecasts are evaluated in local contexts when assessing how useful forecasts can be for increasing the resilience of rural communities. Video clips of predictions made by scientific and traditional forecasters were shown in interviews and focus groups to elicit explanations for why the predictions are sometimes judged to be inaccurate, not useful, or inappropriately communicated by different sectors of the rural population in Ceará, Northeast Brazil. Results indicate that climate forecasts are not simply a decision-making tool that provides information in a one-way transfer from forecaster to user. The meanings and values of predictions are jointly created by both forecasters and their audiences. Predictions and the discussions that surround them are also an important part of expressing social identities and ideas about how the world works. Ineffective predictions are explained here in terms of religious beliefs, environmental change, forecaster identity, interactional context, and cultural practices.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".