Benchmarking on a national scale: the 2007 LibQUAL+<sup>®</sup> Canada experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it