A Delphi Consensus Study on International Best Practice to Promote Resilience, Sustainability, and Safeguard Prisons and Prison Communities Against the Consequences of Climate Change
Bibliographic record
Abstract
Background: Prison systems are routinely excluded from emergency management plans globally, nationally, and locally, increasing vulnerability to climate disasters and posing a risk to health. A global e-Delphi consensus study aimed to define the international best practice to promote resilience, sustainability, and safeguard prisons and prison communities against the consequences of climate change. Research Design: A global e-Delphi consensus approach was used to define the critical components of an effective prison system response to climate change. A consensus was defined a priori as ≥70% participants scoring an outcome from 7 to 9 and <15% scoring it from 1 to 3. In 2024, 4200 International Corrections and Prisons Association members were invited to participate in two online surveys. Results: Of 142 participants, 102 expressing interest completed Round 1 (79% response rate), scoring 40 statements by importance, where 39 exceeded the set threshold of the consensus. Statements were adjusted based on the feedback for Round 2. Of 142 participants, 81 completed Round 2 (72% response rate), scoring 50 statements, with 49 exceeding the set threshold of the consensus. Conclusion and Implications for Practice: Participants agreed on the seven core components of an effective prison system response to climate change: climate principles; climate change and disaster preparedness, planning, and infrastructure protection; partnerships, capacity building, and resources; climate change and disaster response; health-related impacts of climate change; expanding sustainable development approaches; and evaluation, research, and innovation. Further action is required to encourage prison systems to incorporate these components into their policies, guidelines, and initiatives to optimize efforts to safeguard prisons and prison communities.
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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 | 0.248 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".