Working Towards Best Practice in Australian University Libraries: Reflections on a National Project
Bibliographic record
Abstract
‘Best Practice for Australian University Libraries’ was a federally funded project under the Commonwealth Department of Education, Training and Youth Affairs Evaluations and Investigations Program (EIP). The aim was to investigate best practice activities in Australian academic libraries. Reference was also made to relevant best practice activities in selected overseas countries. Best practice activities within Australian academic libraries were considered to encompass the extent of implementation of quality frameworks, the use of benchmarking and performance measurement as tools for the continuous improvement of products, processes and services, and the development of staff competencies and training required for these activities. A Council of Australian University Librarians Working Group is progressing the recommendations of the EIP project report ‘Guidelines for the Application of Best Practice in Australian University Libraries: Intranational and International Benchmarks’ and a parallel ‘Best Practice Handbook for Australian University Libraries' has been published. EIP project reports are published in hardcopy and widely distributed and also made available on the Department of Education, Training and Youth Affairs website.1 Members of the investigating team reflect on the lessons.
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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 | 0.241 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.025 | 0.026 |
| Scholarly communication | 0.030 | 0.016 |
| Open science | 0.007 | 0.036 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".