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Record W2051901721 · doi:10.1080/13549830600785506

Adaptive capacity for climate change in Canadian rural communities

2006· article· en· W2051901721 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueLocal Environment · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsClimate changeAdaptive capacityGeographyEnvironmental resource managementNatural resource economicsEnvironmental planningEnvironmental scienceEconomicsEcology

Abstract

fetched live from OpenAlex

Abstract It is widely acknowledged that promoting the long-term sustainability of rural areas requires an assessment of their capacity to handle stress from a host of external and internal factors such as resource depletion, global trading agreements, service reductions and changing demographics, to name but some. The sustainability literature includes a number of approaches for conducting capacity evaluations but is sparse regarding effective methods and empirical examples. This article provides one approach for assessing community capacity and gives results from its application to a specific Canadian rural community. The authors use general capacity variables and indicators to focus on a particular stress, namely impacts from climate change, and on one type of capacity, namely the capacity to adapt (to such climatic change). A basic framework and profiling tool ('amoeba') for describing the resources underlying community adaptive capacity are offered. The researchers provide a set of indicators reflecting social, human, institutional, natural and economic resources and relate them to climate change adaptation at the community level. Although the indicators cannot be replicated exactly for other rural communities, the essentials of the framework and the profiling tool can. In fact it is hoped that the ideas and example found in this article will encourage researchers to enhance and improve on the methods and results for work on community capacity. Acknowledgements We gratefully acknowledge the support we have received from the Social Science and Humanities Research Council (SSHRC). This includes a Major Collaborative Grant under their Strategic Research Program on Social Cohesion (829-1999-1016) and a Collaborative Research Grant within their Initiative on the New Economy (512-2002-1016). In addition we appreciate the information and resources provided through the Canadian Climate Impacts and Adaptation Research Network (C-CIARN). Notes [1] The New Rural Economy Project Phase 2 (NRE2) is a research and education programme studying rural Canada since Citation1998. It is a collaborative undertaking bringing together rural people, researchers, policy analysts, the business community and government agencies at all levels to identify and address vital rural issues. It is conducted at the national level with historical and statistical data analysis, and at the local level with case studies involving community and household surveys. The NRE's mandate has been extended through 2006 with the help of a major grant from the Initiative on the New Economy Program (INE) of the Social Sciences and Humanities Research Council of Canada. For more information on the NRE, visit <http://nre.concordia.ca/nre2.htm>. [2] The irregularity of the overall shape and the fact that it can change from year to year (if repeated assessments are completed) has led to calling this form of graphing an 'amoeba' approach (ten Brink, Citation1991). [3] Data on the state of roads, bridges and electrical infrastructure were not readily attainable so could not be included in this assessment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.115
GPT teacher head0.275
Teacher spread0.160 · 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