Addressing the needs of the homeless: A San Jose library partnership approach
Bibliographic record
Abstract
San José, California community. The joint city-university library system consisting of San Jose Public and the San Jose State University Libraries won national acclaim for its architectural design (Berry III, 2004), including two sets of main doors which permit entrance from the city side with a clear view into the campus on the university side. This architectural feature permits a person, independent of his or her economic means, to enter the main city library building and “see ” the possibility of entering the campus to earn a degree and advance socially, economically, and professionally. The visual message is verbally underscored during the hundreds of school library tours offered annually. San José Community Context This unique joint city-university library is situated in the heart of California’s high-tech Silicon Valley, the worldwide headquarters for Adobe Systems, eBay, Cisco Systems, Apple Computers, Yahoo, and Google. However, despite the international reputation of this region, significantly different circumstances exist among the diverse campus and community library populations served. The King Library sits in the heart of Santa Clara County, one of the most culturally and ethnically diverse places in the world, where no one racial or ethnic group makes up a majority of the population. County boundaries encompass the City of San José, the nation’s 10th _ largest city. According to a Community Impact Report published in 2006 by the United Way Silicon Valley (available at www.uwsv.org), nearly 40 % of the county’s 1.6 million residents are foreign born, and many more of them are children of immigrants. Approximately one quarter of foreign-born individuals residing in Santa Clara County are from Mexico, and another quarter are from Southeast Asia,
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".