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Enregistrement W4400408323 · doi:10.1073/pnas.2215689121

Showcasing advances and building community in modeling for sustainability

2024· article· en· W4400408323 sur OpenAlexafffund
Noelle E. Selin, Amanda Giang, William C. Clark

Notice bibliographique

RevueProceedings of the National Academy of Sciences · 2024
Typearticle
Langueen
DomaineDecision Sciences
ThématiqueComplex Systems and Decision Making
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaHarvard UniversityNational Science Foundation
Mots-clésSustainabilityEnvironmental planningArchitectural engineeringEnvironmental resource managementBusinessComputer scienceEngineeringEnvironmental scienceEcology

Résumé

récupéré en direct d'OpenAlex

We organized this Special Feature on "Modeling Dynamic Systems for Sustainable Development" to showcase the field's recent advances.Much recent research in sustainability science has mobilized data and theory to better understand systems that include interacting people, technologies, institutions, ecosystems, and both social and environmental processes.A recent National Academies workshop and an Annual Review paper identified several challenges and open questions for the field, stressing the importance of developing and testing new theories to advance knowledge and guide action (1, 2).However, there has been less attention in sustainability science toward integrating modeling with theory and data-focused approaches.Modeling is necessary for making projections about the dynamical implications of our present understanding of nature-society systems-which is essential to determine whether long-term trends in naturesociety interactions are consistent with sustainable development goals and to analyze whether particular interventions (e.g., technologies, policies, behavior) are likely to change those interactions in ways that promote such goals.Many papers, through several decades, have called for better modeling tools to address the connections and feedbacks between natural and social processes (3-6).Much of this work emphasizes shortcomings of models in areas important for sustainability analysis, including capturing multiscale complexity, tracking long-term dynamics, incorporating human agency, and accounting for the generation of novelty.Although this literature highlights significant and persistent gaps in current models, there has been growing interest and action in many research communities to advance science through simulating these aspects of nature-society systems.For example, there has been renewed attention to modeling-related issues in collective efforts to address climate and global change (7, 8), macro-energy systems (9), and social-ecological systems (10).Communities focused on environmental and societal modeling have also increasingly addressed integrated systems (11-13).Recent advances in computational tools and techniques mean that today's state-of-the-art models and analyses look very different from, for example, perceptions of integrated assessment models typically introduced decades ago but still used to address topics such as climate change (14).New state-of-the-art models build on a broader variety of research traditions, are informed and evaluated by novel data sources (including qualitative and quantitative data), make extensive use of growing capacities for data acquisition and analysis, and engage a greater diversity of decision-relevant topics and stakeholders.Many advances are being applied to challenges within specific domains-e.g., to energy systems, food systems, and transportation systems.Others are wellknown within some disciplines-e.g., ecology, economics, or engineering-but not widely adopted in others despite having much to offer there.At the same time, there is much potential for those developing and implementing similar methods to communicate and build community across their respective disciplines and domains.The papers in this Special Feature highlight advances in simulating coupled nature-society systems.We believe that these techniques, if they were more widely adopted, could significantly improve the capacity of sustainability science researchers to test theory, mobilize data, and inform action.Each contribution to the Special Feature addresses a specific area in which novel modeling approaches have demonstrated the capacity to advance theory and insight more broadly.The contributions were selected to be illustrative rather than comprehensive and to facilitate connections across the communities they represent.The process by which we invited and curated papers for this Special Feature reflected this community-building aim.We first conducted a virtual workshop in June 2021, in which roughly 40 invited participants shared their recent modeling advances relevant to sustainable development.We focused on recruiting a diverse cohort of authors, including multiple earlycareer scholars who have developed or used models in a variety of domains relevant to sustainability.Through that process, we refined our proposal for the Special Feature, and conducted an online workshop and weekly virtual seminar series in spring 2022, in which participants presented their papers for comments by the broader group.A number of the papers in the Special Feature represent work catalyzed by connections and ideas generated through this process, with collaborations from authors who had not met prior to the workshop.We hope that similar connections are further facilitated by the publication of the papers in this Special Feature.An opening Perspective by Selin et al. (15) gives an overview of recent progress in this area, arguing that recent work has begun to address longstanding and often-cited challenges in

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,010
score de la tête « metaresearch » (Gemma)0,021
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,051

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0100,021
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,005
Communication savante0,0050,014
Science ouverte0,0030,007
Intégrité de la recherche0,0030,006
Charge utile insuffisante (le modèle a refusé de juger)0,0090,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,238
Tête enseignante GPT0,474
Écart entre enseignants0,236 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2024
Routes d'admission2
Résumé présentoui

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