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Record W3119890852 · doi:10.18438/eblip29805

Homeless Patrons Utilize the Library for More than Shelter but Public Library Services Are Not Designed with Them in Mind

2020· article· en· W3119890852 on OpenAlexvenueno aff
Samantha Kaplan

Bibliographic record

VenueEvidence Based Library and Information Practice · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsPublic relationsSociologyQualitative researchInformation needsPsychologyLibrary sciencePolitical scienceComputer scienceSocial science

Abstract

fetched live from OpenAlex

A Review of: Dowdell, L., & Liew, C. L. (2019). More than a shelter: Public libraries and the information needs of people experiencing homelessness. Library & Information Science Research, 41(4), 100984. https://doi.org/10.1016/j.lisr.2019.100984 Abstract Objective – The study sought to examine the information seeking behavior of homeless patrons and how public libraries meet the needs of homeless patrons. Design – Qualitative phenomenological study. Setting – Public libraries in New Zealand. Subjects – Four homeless patrons who were current library patrons and seven public library workers (senior managers and two front line workers). Methods – Purposive convenience sample of homeless patrons and library workers to participate in face-to-face, semi-structured interviews. The study utilized Creswell's four-step data analysis spiral to produce a synthesis. Main Results – Homeless patrons utilize public libraries for far more than daytime shelter, patronizing the collections, and accessing services. The participating libraries did not have existing policies, practices, services, or staff designed for the needs and wants of homeless people, however, current offerings largely met the needs of homeless patrons. Conclusion – Homeless people use public libraries much like non-homeless patrons and public libraries could develop specialized offerings for them, though they must take care to do so in a way that does not further marginalize this group. Additional research is needed to understand why some homeless people do not utilize the libraries.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.004
Scholarly communication0.0060.006
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.003

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 head0.050
GPT teacher head0.279
Teacher spread0.228 · 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 designObservational
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

Citations1
Published2020
Admission routes1
Has abstractyes

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