Homeless Patrons Utilize the Library for More than Shelter but Public Library Services Are Not Designed with Them in Mind
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".