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Record W6981561079

Environmental Change and Adaptive Capacity in Arctic Communities: an Integrative Approach

2006· other· en· W6981561079 on OpenAlexaboutno aff

Bibliographic record

VenueUSC Research Bank (University of the Sunshine Coast) · 2006
Typeother
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
Fundersnot available
KeywordsAdaptive capacityClimate changeArcticVulnerability (computing)Environmental changeStructuringSettlement (finance)Adaptive strategiesAdaptive managementDPSIR
DOInot available

Abstract

fetched live from OpenAlex

This paper illustrates integration of natural and social science data with the knowledge from community members to document how climate change is being experienced and how policy decisions can enhance capacity to adapt in the future. Consistent with the approaches of Lim et al., 2001, Lange, 2003, and Turner et al., 2003, the vulnerability model provides a robust structure to connect and integrate research findings. The framework has four interconnected components: · What are the environmental conditions (exposures) that are relevant to people and communities and how are they experienced? · What are the adaptive strategies and management systems used to deal with exposures and in what ways are they successful? · What future trends or changes can be expected in environmental conditions that relate to people and communities? · What is the capacity to deal with those changes, and what adaptive measures or policy changes can be taken to enhance that capacity? These questions can be used to direct integrated research programs, but here they provide a means of structuring research findings, some of whichhave been compiled independently. Examples of research in Nunavut and the Inuvialuit Settlement Region (ISR) show how community insights (including traditional knowledge) and scientific knowledge (physical, biological, social and health) are used to assess exposures and to identify practical opportunities for adapting. For example, in the community of Ulukhaktok travel to spring and summer harvesting areas are affected by changes in the timing of sea ice freeze-up and break-up. These changes are consistent with documented temperature increases and records of sea ice dynamics in the Western Arctic during the last half century (McBean, 2005). Harvesters are adapting by traveling to alternative harvesting areas using alternative modes of transportation. Sea ice models predict a continued reduction in sea ice extent which will have further implications for travel and harvesting (Arzel et al., 2006). Hunters have shifted target species, with more harvesting of terrestrial wildlife, which has implications for wildlife management. Another policy connection is the Inuvialuit Harvesters Assistance Program (IHAP) which provides financial assistance to Inuvialuit harvesters to purchase alternative equipment to harvest under the changing ice and land conditions. Policy decisions to address climate change effects are invariably made relevant to multiple stresses that are already pressing. This framework provides a structureto integrate natural science findings with social science, to incorporate traditional knowledge, and to directly connect science to decision-making and policy. It serves the central objective of ArcticNet and can be applied to integrate within regions (IRIS) and to synthesize across regions for an assessment of the Canadian Arctic.

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)0.008
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0100.007
Science and technology studies0.0050.013
Scholarly communication0.0090.014
Open science0.0020.014
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.079
GPT teacher head0.231
Teacher spread0.152 · 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 designObservational
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

Citations0
Published2006
Admission routes1
Has abstractyes

Explore more

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