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Record W2512382046 · doi:10.1080/1088937x.2016.1217095

Arctic sustainability research: toward a new agenda

2016· article· en· W2512382046 on OpenAlexaff
Andrey N. Petrov, Shauna BurnSilver, F. Stuart Chapin, Gail Fondahl, Jessica K. Graybill, Kathrin Keil, Annika E. Nilsson, Rudolf Riedlsperger, Peter Schweitzer

Bibliographic record

VenuePolar Geography · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsMemorial University of NewfoundlandUniversity of Northern British Columbia
FundersDivision of Polar Programs
KeywordsArcticSustainabilityPolitical scienceThe arcticEnvironmental resource managementEnvironmental planningGeographyEcologyOceanographyEnvironmental science

Abstract

fetched live from OpenAlex

The Arctic is among the world’s regions most affected by ongoing and increasing cultural, socio-economic, environmental and climatic changes. Over the last two decades, scholars, policymakers, extractive industries, local, regional and national governments, intergovernmental forums, and non-governmental organizations have turned their attention to the Arctic, its peoples and resources, and to challenges and benefits of impending transformations. The International Conference on Arctic Research Planning (ICARP) has now transpired three times, most recently in April 2015 with ICARP III. Arctic sustainability is an issue of increasing concern within the Arctic and beyond it, including in ICARP endeavors. This paper reports some of the key findings of a white paper prepared by an international and interdisciplinary team as part of the ICARP-III process, with support from the International Arctic Science Committee Social and Human Sciences Working Group, the International Arctic Social Sciences Association and the Arctic-FROST research coordination network. Input was solicited through sharing the initial draft with a broader network of researchers, including discussion and feedback at several academic and community venues. This paper presents a progress report on Arctic sustainability research, identifies related knowledge gaps and provides recommendations for prioritizing research for the next decade.

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)0.003
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.913
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
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 head0.105
GPT teacher head0.403
Teacher spread0.298 · 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

Citations44
Published2016
Admission routes1
Has abstractyes

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