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Record W7062224483

Stressors or Stresses? Addressing the Nuanaces of Multiple Stressors in Human Dimensions of Climate Change Scholarship in the Arctic

2016· other· en· W7062224483 on OpenAlexaboutno aff

Bibliographic record

VenueUSC Research Bank (University of the Sunshine Coast) · 2016
Typeother
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipClimate changeStressorContext (archaeology)Psychological resilienceAdaptive capacityAdaptation (eye)
DOInot available

Abstract

fetched live from OpenAlex

This poster examines key conceptual nuances of multiple stressors in human dimensions of climate change (HDCC) scholarship. They include: (i) the internal vs. external nature of stressors, (ii) context disaggregation, and (iii) the application of the nirvana fallacy. Inuit are experiencing climate change in the context of multiple climatic and non-climatic stressors that are already affecting lives and livelihoods. Research shows that in many instances non-climatic stressors can be strategic policy entry points for enhancing adaptive capacity to deal with current and future climate change; supporting efforts that increase financial, health, educational and cultural capacity in communities has been shown to inadvertently enhance the capacity of individuals and the community to deal with current and expected future climate change risks. To date, however, the conceptualisation of multiple stressors in HDCC scholarship has been unclear, often resulting in a simplistic cause and effect interpretation of how Inuit experience and respond to climate change. It is argued that by exploring and unpacking some of the key nuances of the multiple stressors concept, we can advance HDCC scholarship and contribute to the development and implementation of effective climate change adaptation strategies in the Arctic and elsewhere around the world. This research is part of ArcticNet Project “Community Vulnerability, Adaptation and Resilience to Climate Change in the 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 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.145
GPT teacher head0.339
Teacher spread0.194 · 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 teacher head, 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueUSC Research Bank (University of the Sunshine Coast)Same topicNuclear and radioactivity studiesFrench-language works237,207