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Record W4284959846 · doi:10.31235/osf.io/vjmk4

Public library building and development: Understanding community consultation and the design process

2022· preprint· en· W4284959846 on OpenAlexaff
Simon Wakeling, Monique Shephard, Philip Hider, Hamid R. Jamali, Jane Garner, Mary Coe

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsFuture Earth
FundersCharles Sturt University
KeywordsParticipatory designProcess (computing)Citizen journalismPublic relationsCommunity developmentCommunity engagementCommunity designSociologyCommunity buildingPolitical scienceKnowledge managementEngineeringComputer scienceOperations management

Abstract

fetched live from OpenAlex

There is an increasing focus on the public library’s role as a place of and for the community, that should have at its heart the needs of that community. In this respect the development of new or renovated public libraries offers an opportunity for the design of these new buildings to reflect the needs and wants of the communities they serve. The aim of this project was to develop an in-depth understanding of the views and approaches of both librarians and architects involved in public library development projects in Australia. Using data gathered through semi-structured interviews this paper explores notions of community-focused design and co-design and the implications of involving the community in the process of library design for six public library development projects. Participants described a range of community engagement activities, relating benefits and challenges to the community consultation process. However, it was noted that there was also a curated nature to this input, and there was little evidence of community engagement extending beyond consultation to truly participatory design.

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)0.091
metaresearch head score (Gemma)0.078
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 score0.091
Threshold uncertainty score0.483

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0910.078
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0300.065
Scholarly communication0.0230.024
Open science0.0060.024
Research integrity0.0090.007
Insufficient payload (model declined to judge)0.0060.001

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 head0.317
GPT teacher head0.348
Teacher spread0.032 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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