Bibliographic record
Abstract
Research into the library as place investigates the role of public library buildings as destinations, physical places where people go for various reasons ranging from making use of the library’s resources and services or seeking to fulfill an information or reading need to less easily identified reasons that may include using the library’s building as a place to make social or business contacts, to build or reinforce community or political ties, or to create or reinforce a personal identity. This study asks: How are one rural U.S. county’s public library buildings functioning as places? The answer is derived from answers to sub-questions about adult library users, user and staff perceptions of library use, and observed use of library facilities. The findings are contextualized using a framework built of theories from human geography, philosophy, sociology, and information studies. This case study replicates a mixed-methods case study conducted at the main public libraries in Toronto and Vancouver in the late 1990s and first reproduced in Halifax, Nova Scotia in 2006. It tests methods used in large urban settings in a rural, small-town environment. This study also expands on its antecedents by using thematic analysis to determine which conceptualizations of the role of the public library as place are most relevant to understanding this community’s use of its public library buildings as places. The study relies on quantitative and qualitative data collected via surveys and interviews of adult library users, interviews of library public service staff members, structured observations—seating sweeps—of people using the libraries, and analysis of selected documents. The five sets of data are triangulated to answer the research subquestions. Thematic analysis derived from the conceptual framework finds that public realm theory informs the relationships that develop between library staff members and adult library users over time. The study finds that the libraries serve their communities as informational places and as familiarized locales rather than as third places, and that the libraries support the generation of social capital for their users.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".