MétaCan
Menu
Back to cohort
Record W1589092715 · doi:10.1002/wcc.315

Glaciers and society: attributions, perceptions, and valuations

2014· article· en· W1589092715 on OpenAlexafffund
Karine Gagné, Mattias Borg Rasmussen, Ben Orlove

Bibliographic record

VenueWiley Interdisciplinary Reviews Climate Change · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversité de Montréal
FundersDanish Agency for Science and Higher EducationSocial Sciences and Humanities Research Council of CanadaNational Science Foundation
KeywordsAttributionPerceptionGlacierSocial psychologyPolitical sciencePsychologyGeographyPhysical geography

Abstract

fetched live from OpenAlex

As icons of a world set in motion by human action, glaciers are often highlighted as quintessential evidences of global climate change. Although there is a general agreement among scientists that glaciers around the world are receding, much of the discussions on the subject tend to be oriented toward technological methodologies. Yet, as elements of the landscape, glaciers are strongly integrated to various societies around the world in ways that exceed their role as provider of fundamental sources of water. The relation between glaciers and societies is therefore marked by processes of attribution, perception, and valuation by local and distant actors. As a consequence, as they recede, glaciers often become the loci of interactions between actors of various scales. But besides melting, glaciers also transform from being objects of local to national and global concern. This is particularly true when esthetic and economic values are assigned to glaciers. Real and perceived changes in the form, reach and out‐flow of water impact the local populations, and shape the kinds of action undertaken by communities, local actors, state authorities, and international organizations. The paper concludes by arguing that place‐based research is fundamental to discuss a global environmental phenomenon such as glacier recession. WIREs Clim Change 2014, 5:793–808. doi: 10.1002/wcc.315 This article is categorized under: Climate, History, Society, Culture > Ideas and Knowledge Vulnerability and Adaptation to Climate Change > Learning from Cases and Analogies Social Status of Climate Change Knowledge > Sociology/Anthropology of Climate Knowledge

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)0.024
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 score0.009
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.005
Science and technology studies0.0020.025
Scholarly communication0.0090.013
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.072
GPT teacher head0.307
Teacher spread0.235 · 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

Citations7
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueWiley Interdisciplinary Reviews Climate ChangeSame topicCryospheric studies and observationsFrench-language works237,207