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Record W4414760646 · doi:10.18357/big_r62202522317

Innovation and Vulnerability at the Italian–Swiss Border: The Cervinia–Zermatt Cable Car

2025· article· en· W4414760646 on OpenAlexvenueno aff
Paola Malaspina

Bibliographic record

VenueBorders in Globalization Review · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityVulnerability (computing)Stewardship (theology)StakeholderStakeholder engagementResilience (materials science)Environmental stewardshipEnvironmental management systemVulnerability assessment

Abstract

fetched live from OpenAlex

This policy report examines the Matterhorn Alpine Crossing (MAC), an innovative cable car system that connects Italy and Switzerland through the Alps. Launched in 2023, this project showcases the potential for modern engineering to enhance regional connectivity and economic growth, while also posing significant challenges in terms of environmental conservation and operational efficiency. The report draws on a range of interdisciplinary literature, focusing on environmental management and sustainable infrastructure development. This analysis reveals that the infrastructure encountered critical challenges stemming from its environmental impact and the need for improved management practices. The findings advocate for a comprehensive approach that prioritizes environmental sustainability and robust stakeholder engagement to ensure the project’s long-term viability. Proposed recommendations emphasize the importance of adaptive management strategies that respond to ongoing environmental and operational challenges. The report suggests that with thoughtful planning and committed execution, MAC could become a benchmark for integrating technological innovation with environmental stewardship in sensitive regions.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.742
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.410
Teacher spread0.392 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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