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Record W621282884

How and why public libraries can, should and do facilitate the use of the Internet by the homeless: a look at the programs, barriers and political climate

2010· other· en· W621282884 on OpenAlexaboutno aff
Julie Ann Winkelstein, Edwin-Michael Cortez

Bibliographic record

VenueDSpaceUnipr (University of Parma) · 2010
Typeother
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsThe InternetPoliticsInternet accessInternet privacyPublic relationsPolitical scienceBusinessSociologyWorld Wide WebComputer science
DOInot available

Abstract

fetched live from OpenAlex

The use of the Internet by library patrons in public libraries has risen rapidly. Bertot et al. (2006) report that as of 2006, 98.9% of U.S. public library branches were connected to the Internet and 98.4% of these offered public Internet access. The number of homeless in the United States and other countries has also increased, with numbers ranging from an estimated 334,744 sheltered homeless on an average day in the United States to between 200,000 and 300,000 in Canada. When including the “hidden homeless” this number rises to as high as 800,000 in Great Britain. Public libraries can and do provide comfortable and safe daily environments for many of the homeless. In addition, these libraries can support and encourage the efforts of the homeless to use the Internet to find and apply for jobs, to stay connected to friends and family, to create websites and blogs, to do homework, and to do research on such topics as health and housing. The purpose of this paper is to examine the role of the public library in providing Internet access to the homeless. Four research questions are posed: 1. How and why do the homeless use the Internet; 2. What are the barriers to this access; 3. How can public libraries facilitate the use of the Internet by the homeless; and, 4. How does the political climate affect the use of the Internet by the homeless.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.009
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.067
GPT teacher head0.243
Teacher spread0.177 · 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 designNot applicable
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
Published2010
Admission routes1
Has abstractyes

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