University Community Engagement and the Strategic Planning Process
Bibliographic record
Abstract
Objectives- To understand how university libraries are engaging with the university community (students, faculty, campus partners, administration) when working through the strategic planning process. Methods- Literature review and exploratory open-ended survey to members of CAUL (Council of Australian University Librarians), CARL (Canadian Association of Research Libraries), CONZUL (Council of New Zealand University Librarians), and RLUK (Research Libraries UK) who are most directly involved in the strategic planning process at their library. Results- Out of a potential 113 participants from 4 countries, 31 people replied to the survey in total (27%). Libraries most often mentioned the use of regularly-scheduled surveys to inform their strategic planning which helps to truncate the process for some respondents, as opposed to conducting user feedback specifically for the strategic plan process. Other quantitative methods include customer intelligence and library-produced data. Qualitative methods include the use of focus groups, interviews, and user experience/design techniques to help inform the strategic plan. The focus of questions to users tended to fall towards user-focused (with or without library lens), library-focused, trends & vision, and feedback on plan. Conclusions- Combining both quantitative and qualitative methods can help give a fuller picture for librarians working on a strategic plan. Having the university community join the conversation in how the library moves forward is an important but difficult endeavour. Regardless, the university library needs to be adaptive to the rapidly changing environment around it. Having a sense of how other libraries engage with the university community benefits others who are tasked with strategic planning
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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 | 0.034 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".