Benchmarking on a national scale: the 2007 LibQUAL+<sup>®</sup> Canada experience
Bibliographic record
Abstract
Purpose In 2006/2007, the Canadian academic library community came together in the largest national LibQUAL+® consortium to conduct ARL library service quality survey. This paper aims to address how and why the national consortial project came about, the challenges for recruiting and managing participants, and what was learnt, together with possible future directions. Design/methodology/approach This paper uses a case study approach. Findings The research touches on the challenges planning and implementing LibQUAL+® with such a large, diverse consortium, with its bilingual mandate and multiple library types, and what made the project successful and its limitations. Practical implications The most apparent accomplishment of this project was successful collection of a large, diverse data set for comparative analysis of services and facilities – a meaningful data set both for individual libraries seeking appropriate Canadian comparators and for analyses by region, institutional categories, etc. Originality/value A valuable result of the project was to engage more Canadian academic libraries in the process of service assessment. CARL's bi‐lingual consortium approach will provide a valuable example for other national organisations attempting to carry out similar projects.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".