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
Bibliographic record
Abstract
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.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".