Measuring What Matters: Organisational Effectiveness by the Numbers in One Canadian Public Library
Bibliographic record
Abstract
The presentation will concentrate on a decade of using statistics and measurement methods to manage a large urban public library (serving 650,000 with 16 locations) in a period of rapid growth and development of its services. The Mississauga Library System doubled in ten years and yet successfully met all the challenges of growth to remain the top rated local service. Through a formal management process composed of several key activities including performance management, strategic management, organisational health, and value management, the Library identified key statistics and key quality as well as quantity performance indicators and sought to affect those positively through changes in inputs and outputs in key areas of service expansion, innovation, continuous improvement, and efficiencies. The presentation will review the components of the Library’s formal management process and the measurement methods used as well as a multi-year approach including major service plans and their multi-year budgets. Examples of the statistics collected, the methodologies used, and performance indicators devised for each part of the process will be examined along with the Library’s evolving vision for and definition of ultimate “success” – superior service at a reasonable cost. As well, the key Canadian library statistics – public as well as other – will be reviewed as the background to one large public library’s pursuit of effectiveness. The issues of the Canadian library scene will be covered along with current national practices and the unmet measurement needs today.
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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 | 0.025 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".