Abstract ‘‘Sweeping’ ’ the library: Mapping the social activity space of the public library 1
Bibliographic record
Abstract
Although libraries are public spaces in which individuals engage in a range of social and informational activities, few researchers in library and information science use ethnographic approaches to study users ’ experiences in these settings. This article describes spatial analysis techniques used by geographers and other researchers of social space. It examines the ways in which these techniques may be used to map the physical layout of libraries and information centers, and patrons ’ uses of those spaces. The article focuses on one observational approach (the ‘‘seating sweeps’ ’ method) used to study individuals ’ use of central public libraries in two large Canadian cities. In addition to a description of the design and implementation of the method, the article presents some of the study’s findings that support the utility of this method for facilities redesign or planning to accommodate patrons ’ information behaviors and usage patterns and to emphasize the central library as a vibrant and vital public space.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".