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Record W3169194934 · doi:10.17645/up.v6i2.3911

Flying Less for Work and Leisure? Co-Designing a City-Wide Change Initiative in Geneva

2021· article· en· W3169194934 on OpenAlexaboutno aff
Marlyne Sahakian, Malaïka Nagel, Valentine Donzelot, Orlane Moynat, Wladyslaw Senn

Bibliographic record

VenueUrban Planning · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsnot available
FundersUniversité de Genève
KeywordsGeneral partnershipVariety (cybernetics)Public relationsValue (mathematics)Psychological interventionLegislationWork (physics)Quarter (Canadian coin)Political scienceTypologySociologyBusinessEngineeringPsychologyGeographyLaw

Abstract

fetched live from OpenAlex

Geneva prides itself on being an international city, home to the United Nations and international organizations. The airport plays an important role in this image, tied to a quest for hypermobility in an increasingly globalized society. Yet, mobility accounts for close to one quarter of the territory’s carbon emissions, with flights responsible for 70% of these emissions. With recent legislation that includes ambitious targets for net zero carbon emissions by 2050, the role of air travel can no longer be ignored. In 2020, a partnership was formed between the City, the University of Geneva, and a community energy association to explore the possibility of co-designing a city-wide change initiative, focused on reducing flights through voluntary measures. The team consulted with a variety of actors, from citizens who fly for leisure, to those who fly for professional reasons, with a spotlight on academic travel. A review of the scientific and grey literature revealed what initiatives already exist, leading to a typology of change initiatives. Inspired by this process, we then co-designed a series of workshops on opportunities for flying less in Geneva. We demonstrate the value of going beyond an ‘individual behaviour change’ approach towards understanding change as embedded in socio-material arrangements, as well as identifying interventions that seek to address both negative and positive anticipated outcomes. We conclude with insights on how a social practice approach to understanding mobility reveals both material and immaterial challenges and opportunities, involving infrastructures and technologies, but also social norms and shared meanings.

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)0.015
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0090.013
Scholarly communication0.0080.008
Open science0.0030.011
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.257
GPT teacher head0.347
Teacher spread0.090 · 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 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

Citations12
Published2021
Admission routes1
Has abstractyes

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