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Record W2579935085 · doi:10.4000/rga.3330

What Policy Evidence for a European Strategy of Sustainable Development in Mountain Regions?

2016· article· en· W2579935085 on OpenAlexaff
Erik Gløersen, Clemens Mader, Engelbert Ruoss

Bibliographic record

VenueRevue de géographie alpine · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional resilience and development
Canadian institutionsInstitute on Governance
FundersSlovenska Akademija Znanosti in Umetnosti
KeywordsNexus (standard)SustainabilitySustainable developmentEnvironmental resource managementMulti-level governanceCorporate governanceNatural resourceMainstreamPolitical scienceEnvironmental planningRegional scienceBusinessGeographyEconomicsEcologyComputer science

Abstract

fetched live from OpenAlex

The aspiration to implement evidence-based policies has led to an increased focus on quantitative indicators and targets defined at the European level as instruments for designing policy measures and assessing their impact. The authors argue that this constrains debate and has hindered the elaboration of a proactive European strategy for sustainable development in mountain regions. Mountain territories have highly diverse social, economic and physical characteristics; their shared traits in terms of ecological fragility, economic development challenges and exposure to natural hazards are not reflected in mainstream datasets. Two complementary instruments are proposed to produce and present evidence for sustainable resource management and processes: the Nexus Model and the Sustainability Profile Matrix. Both tools entail using evidence that is adapted to the social and economic characteristics, potentials and challenges of each locality or region. At the same time, they make compilations of evidence at the transnational and European levels possible. The objective is to enable local, national and transnational authorities to use territorial diversity as a lever in their policies, within multilevel governance in human, economic and natural resource management.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.066
GPT teacher head0.282
Teacher spread0.216 · 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 designTheoretical or conceptual
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
Published2016
Admission routes1
Has abstractyes

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