Promoting Older Adults’ Engagement in Disaster Settings: An Introduction to the Special Issue
Bibliographic record
Abstract
Globally, surging extreme events and the escalating aging population present ongoing and severe challenges to the full spectrum of international community development (for example, social, health, and economic) (Dee 2024 ). Over the past 20 years, climate-induced and environmental disasters worldwide have caused over 1.3 million casualties and left more than 4.4 billion people injured, homeless, and/or in need of emergency assistance, with total direct economic losses approaching USD 3 trillion (UNDRR 2018 ). The rising human and economic costs have compelled international communities to prioritize resilience enhancement. Furthermore, the United Nations (UN 2019 ) reported that the global population of adults aged 65 and older will almost double from 9% in 2019 to 16% in 2050. Some countries, such as Greece, Korea, and Japan, have an even faster aging rate than the global average (World Economic Forum 2020 ).
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".