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Are Climate Change Adaptation Policies a Game Changer?

2016· book-chapter· en· W4248810828 on OpenAlexaffabout
Chris G. Buse

Bibliographic record

VenueIGI Global eBooks · 2016
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsClimate changeTypologyPublic healthPolitical scienceAdaptation (eye)Variety (cybernetics)Climate change adaptationEnvironmental planningPublic relationsGeographyPublic administrationEnvironmental resource managementMedicinePsychologyEconomicsNursingComputer science

Abstract

fetched live from OpenAlex

The health impacts of climate change have received significant attention in the international scholarly literature. Despite this, there is an absence of research evaluating existing policies aimed at promoting and protecting population health. This chapter provides an implementation analysis of the Ontario Public Health Standards (OPHS), 2008/2014--the provincial policy statement that governs mandatory public health activities in the province which includes taking action on climate change. This chapter responds to two specific questions: First, how are Ontario's 36 regional health units interpreting and implementing this policy statement; and second, how are those interpretations translated into practice. Using a web-scan and in-depth interviews with practitioners from twenty Ontario health units, this paper presents four interpretations of the OPHS, a typology of best practices related to regional adaptation, and policy recommendations to bolster domestic and international adaptive capacity to emerging infectious diseases associated with climate change, and a variety of other health-related climate impacts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.365
Threshold uncertainty score0.726

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.010
Scholarly communication0.0060.005
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.110
GPT teacher head0.309
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2016
Admission routes2
Has abstractyes

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