What is Helpful (and Not) in the Strategic Planning Process? An Exploratory Survey and Literature Review
Bibliographic record
Abstract
Strategic planning is a necessary undertaking for many university libraries. Through a literature review and an open-ended, exploratory survey to university libraries in Australia, Canada, New Zealand, and United Kingdom, the researcher was able to get a sense of nuance and importance of some of the parts of the process. Themes are organized into who worked on the process, the timeframe of the plan and process, prioritization and focus, environmental scanning, university plan alignment, and assembling the plan. Understanding what worked (and what did not) can help others who are tasked with taking on lead roles in the strategic planning process, and can enable libraries to create a strategic plan that works best for their staff, users, and institution.
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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 | 0.049 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".