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Record W4402465610 · doi:10.29173/cais1839

Reimagining “Palaces for the People”: A Critical Review of Public Libraries’ Engagement with the Asocial Society

2024· review· en· W4402465610 on OpenAlexaffvenue
Nicole Dalmer, Paulette Rothbauer, Pamela J. McKenzie, Kevin Oswald, Ebenezer Martin‐Yeboah, Anne Goulding

Bibliographic record

VenueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI · 2024
Typereview
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsWestern UniversityMcMaster University
Fundersnot available
KeywordsPublic engagementPolitical scienceSociologyMedia studiesEnvironmental ethicsSocial sciencePublic relationsPhilosophy

Abstract

fetched live from OpenAlex

The “loneliness epidemic,” a public health crisis characterized by reports of higher levels of social isolation and loneliness, has been attributed to features of modern living, including urbanization and the increase of one-person households. Public library workers are contending with individuals navigating this crisis. Findings are presented from a state-of-art literature review focussed on recent English-language, peer-reviewed studies (n=235) of public library programming, services, technology and policies in the context of trends in the contemporary asocial society. Across published research, public libraries fostered connection through the following means: encouraging feelings of belonging, creating connections through technology, reinforcing cultural identities, creating safe physical spaces, addressing issues of accessibility, creating new educational programming, and creating new recreational/social programming. The findings allow for a reimagining of the roles of public libraries but not without a reckoning about workplace culture and workloads of library staff.

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)0.006
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0080.010
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.116
GPT teacher head0.361
Teacher spread0.245 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSISame topicLibrary Science and AdministrationFrench-language works237,207