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Enregistrement W7094548960

Designing for Micropolitan Areas: A Public Library Design Manual for Adaptive and Circular Applications

2023· article· W7094548960 sur OpenAlexaboutno aff

Notice bibliographique

RevueLincoln (University of Nebraska) · 2023
Typearticle
Langue
DomaineComputer Science
ThématiqueHistory of Computing Technologies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMetropolitan areaQuality (philosophy)PopulationDistribution (mathematics)LiteracyCensusQuarter (Canadian coin)Investment (military)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

A micropolitan statistical area refers to a geographic region in the United States that has at least one urban cluster of population between 10,000 and 50,000 people, as defined by the US Census Bureau. According to the 2019 Fiscal Year Public Libraries Survey, more than three-fourths of public libraries serve areas with fewer than 25,000 people in the U.S. (Frehill, et al. 2021). However, as new design techniques and advanced services are developed, they tend to be primarily implemented in libraries serving metropolitan areas, which only serve around a quarter of the US population, leading to an unequal distribution of resources and potential disadvantages for residents of micropolitan statistical areas. This presents an issue of unequal distribution of resources, as micropolitan areas often lack the funding and resources to invest in high-quality library design and amenities, resulting in lower quality facilities compared to those in metropolitan areas. The lack of funding within micropolitan statistical areas can further contribute to the digital divide and hinder access to information for those living in these regions. In these micropolitan statistical areas, there is often a lack of libraries or a general access to books, creating book deserts which can result in lower literacy rates, poorer educational outcomes, and overall lower quality of life for residents. In order to address these issues and promote greater literacy and well-being, it is crucial to increase investment in public libraries in micropolitan areas, including expanding access to books and other valuable services. This thesis proposes to address the challenges of constructing public libraries that are both high-quality and affordable. A design manual focused on adaptive reuse and circular design techniques will offer a solution to reduce the building cost of new public libraries, without compromising the quality of design. By integrating higher quality design into the process, this manual will help create a higher quality of life for library patrons and staff alike. The manual would include techniques for creating affordable high-quality design, and would be structured as a flexible system that can be tailored to a range of budgets and needs. Similar to a choose your own adventure book, users would build from existing conditions and techniques from the ground up, with thousands of possible combinations. Such a manual will help address the issue of inequitable distribution of resources by providing a tool for creating public libraries that are accessible to a wider range of communities, including those in micropolitan statistical areas. This thesis proposes a new design process that integrates a variety of information in a new format, including a catalog of systems that creates a mode of creation for quick generation of building design. By relating existing data in a new format, this thesis provides a representational design process to solve a design problem. The proposed design manual focuses on adaptive reuse and circular design techniques to lower the building cost of new public libraries, while still integrating higher quality design to create a higher quality of life. Ultimately, this thesis provides an innovative approach to building design, which not only benefits the field of architecture but also positively impacts public libraries in the US by providing affordable and high-quality design solutions that can be customized to meet the unique needs of different communities.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,634
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,001
Communication savante0,0000,001
Science ouverte0,0020,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,054
Tête enseignante GPT0,234
Écart entre enseignants0,180 · 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 tête enseignante, pas un consensus.

Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreMéthodes

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

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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