Anchoring Change: Using the Kotter Change Management Framework to Analyze & Facilitate Change in Academic Libraries
Bibliographic record
Abstract
Changes in the higher education landscape are happening more rapidly than ever and require academic libraries to engage with users in new and different ways. Libraries participate in digital scholarship, lead textbook affordability and OER initiatives, create makerspaces, and more. These new and different expectations require library leaders, managers and employees at every level to facilitate change in a variety of situations that range in complexity and are almost always messy. Learn about trends across a collection of twenty change stories in academic library settings, including two- and four-year institutions in the United States and Canada. At the same time, explore Kotter’s (1996, 2012) Eight-Stage Process of Creating Major Change as outlined in his book, Leading Change. This will serve as the framework to examine changes that involve technology, strategic planning, culture shifts, reorganizations, and adapting to new roles. This session will utilize a case study approach to examine change at the programmatic level and organizational level. Whether you are a library administrator, a middle manager or an active participant in the daily work of a library, this session will provide a deep dive into a change framework to use before, during or after a change initiative at your institution.\nPresented at the ALAO Virtual Conference on October 29, 2020.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".