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Record W2119762984 · doi:10.14430/arctic4365

Vulnerability to Freshwater Changes in the Inuit Settlement Region of Nunatsiavut, Labrador: A Case Study from Rigolet

2014· article· en· W2119762984 on OpenAlexvenueaboutno aff
Christina Goldhar, Trevor Bell, Johanna Wolf

Bibliographic record

VenueARCTIC · 2014
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsVulnerability (computing)Adaptive capacityGeographyClimate changeWatershedArcticSettlement (finance)Resource (disambiguation)Environmental changePsychological resilienceEnvironmental resource managementEcologyEnvironmental scienceBusiness

Abstract

fetched live from OpenAlex

Drawing on vulnerability approaches from the climate change literature, this paper explores the vulnerability of residents of the community of Rigolet, Nunatsiavut, Labrador, to changes in freshwater. Our approach emphasizes local preferences and values. We analyze the results from 89 household interviews (88% response) and targeted interviews in Rigolet to consider the human experience of climate variability and change. Residents report that changes in the spatial and temporal distribution of freshwater are currently challenging their ability to access preferred drinking water and food sources and are adding to the financial barriers that restrict their time spent on the land. The results of our study suggest that Rigolet residents are successfully adapting to existing freshwater changes in their watershed, though these adaptations have not come without sacrifice. The adaptive capacity of Rigolet residents has been supported by resource flexibility and experience-based knowledge of freshwater variability within their watershed, among other factors. Findings suggest that the exposure of sub-Arctic and Arctic communities to freshwater changes and their capacity to adapt are largely shaped by the lifeways of residents and the manner and degree to which they are dependent on local freshwater systems.

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)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.051
GPT teacher head0.368
Teacher spread0.317 · 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 designQualitative
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

Citations35
Published2014
Admission routes2
Has abstractyes

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Same venueARCTICSame topicIndigenous Studies and EcologyFrench-language works237,207