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Record W7094548960

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

2023· article· W7094548960 on OpenAlexaboutno aff

Bibliographic record

VenueLincoln (University of Nebraska) · 2023
Typearticle
Language
FieldComputer Science
TopicHistory of Computing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaQuality (philosophy)PopulationDistribution (mathematics)LiteracyCensusQuarter (Canadian coin)Investment (military)
DOInot available

Abstract

fetched live from 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.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.634
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.054
GPT teacher head0.234
Teacher spread0.180 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2023
Admission routes1
Has abstractyes

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