Using a Geographic Information System to Analyze Public Library Performance Measures
Bibliographic record
Abstract
This exploratory study analyzed the spatial distribution of selected measures from the 2003 public library statistics for the province of Ontario, Canada, using the geographic information system (GIS) ArcGIS Desktop. The data consisted of performance indicators covering the broad categories of general information, staff, collections, service transactions, and expenditures. Selected key ratios that normalized the data for the population served among the 303 Ontario public libraries in the data set were examined to try and identify spatial patterns and regional differences, and analyses were also performed after categorizing the public libraries into intervals based on the size of the population served. As well, an additional goal of this study was to make an assessment regarding the utility of using geographic information systems to analyze public library performance measures. The results indicated that for certain performance measures there are differences in normalized values both regionally as well as among population served categories, and that visualizing library performance measure data is a powerful technique for revealing statistical patterns. Consequently, geographic information systems have the potential to reveal hidden dimensions within library performance measure data, and they can be effectively used to analyze and better understand library metrics in the quest to articulate the value and importance of public libraries.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".