Three Strategies to Strengthen Child Disaster Research
Bibliographic record
Abstract
Today's children are expected to experience two to seven times more disaster events than their grandparents (Thiery et al., 2021).Climate-related disasters such as floods, hurricanes, and wildfires are key drivers of the outsized impacts disasters will have on children.Children are at heightened risk for negative consequences following disasters.Children often have limited ability to protect themselves, and they rely on adults for support and assistance.Therefore, it is important to consider children's vulnerability and to support and protect them during and after disasters.Addressing the intersection between climate-related disasters and children's vulnerability requires a strong research pipeline, as well as the expertise of multiple fields (e.g., psychology, public health, engineering, and planning).This commentary highlights three ways to strengthen the child disaster research pipeline.
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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 | 0.145 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.021 | 0.020 |
| Scholarly communication | 0.029 | 0.017 |
| Open science | 0.008 | 0.028 |
| Research integrity | 0.031 | 0.028 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".