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Record W2145243623 · doi:10.1111/cag.12203

Coastal climate change and aging communities in Atlantic Canada: A methodological overview of community asset and social vulnerability mapping

2015· article· en· W2145243623 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsMount Saint Vincent UniversityDalhousie University
FundersPublic Health Agency of Canada
KeywordsGeographyClimate changeVulnerability (computing)PopulationAsset (computer security)Environmental resource managementEnvironmental planningOceanographySociologyEnvironmental science

Abstract

fetched live from OpenAlex

Coastal climate change is challenging communities to adapt. More frequent and extreme weather events leading to coastal area flooding and other hazards can present a risk for residents and the infrastructure and services they rely on. This is particularly the case for vulnerable populations such as seniors. Nova Scotia is experiencing this confluence of factors; it has rural and remote coastal communities and the oldest population of any province in Canada. Our spatial study examines these dynamics in five rural and small town municipalities in Lunenburg and Annapolis counties. We combine population model projections and coastal sea rise scenarios to the year 2025–2026 with community asset, infrastructure, and residential property mapping and a review of municipal policies. We forward a framework for understanding coastal climate change impacts on key infrastructure, services, and assets that are relied upon by an older population as well as the current and potential municipal planning responses. We find that critical assets important to older populations are impacted by coastal climate change in our study areas and time frame. This article shares our research methods and findings with the aim of helping communities map change and plan for the future. Les changements climatiques en milieu côtier et le vieillissement des communautés du Canada atlantique: un survol méthodologique de la cartographie des ressources communautaires et de la vulnérabilité sociale Les communautés sont confrontées à des défis d'adaptation aux changements climatiques en milieu côtier. Des phénomènes météorologiques exceptionnels et répétés provoquant des inondations et d'autres dangers dans les zones côtières peuvent exposer à des risques les résidents et les infrastructures et services dont ils dépendent. Les personnes âgées comptent parmi les populations vulnérables particulièrement touchées. Ces facteurs sont réunis en Nouvelle‐Écosse qui abrite des communautés côtières rurales et éloignées et la population la plus âgée de toutes les provinces canadiennes. Cette étude spatiale aborde la dynamique qui s'opère dans cinq petites municipalités rurales situées dans les comtés de Lunenburg et d'Annapolis. Les modèles de projection démographique et les scénarios de hausse du niveau marin en zones côtières à l'horizon 2025–2026 sont mis en parallèle avec la cartographie des ressources communautaires, des infrastructures, et des propriétés résidentielles ainsi qu'un examen des politiques municipales. Un cadre est ensuite proposé afin de comprendre les conséquences des changements climatiques en milieu côtier sur les infrastructures, services et ressources dont la population vieillissante dépend, de même que les mesures actuelles et potentielles prises par les municipalités en matière d'aménagement du territoire. Le constat qui se dégage est que les effets des changements climatiques en milieu côtier affectent les ressources indispensables pour les populations âgées dans les territoires et pour la période sous étude. L'intérêt de diffuser nos méthodes de recherche et résultats est de soutenir les communautés pour cartographier les changements et prévoir l'avenir.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.339
GPT teacher head0.338
Teacher spread0.001 · 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