Bibliographic record
Abstract
Purpose The paper seeks to document the process and strategies used to create transformational change at the operational, organizational and cultural level. Design/methodology/approach The vision of transformational change was guided by three principles: building it today, adding value, and aligning with the university's strategic plan. Findings During a 2008‐2009 internal review it became clear that current services and systems were inhibiting the ability to move forward. To overcome this inertia, eight strategies were developed to lay the foundation for transformational change. These included: creating a framework for change, leveraging outside expertise, building a leadership team, designing a new organizational structure, influencing organizational culture, managing transition, forming operational teams and workgroups, and reflections. The greatest challenge has been to manage library staff fears and expectations. Dealing with both passive and active resistance has required flexibility and a commitment on the part of library administration to engage staff in an ongoing dialogue to clarify the vision and to encourage staff to see change as serving both the library's interests and their own self‐interest. Originality/value The value of this paper is in showcasing tools and strategies for transforming an academic library's organizational culture and structure.
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.
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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".