The Expert Library: Staffing, Sustaining, and Advancing the Academic Library in the 21st Century
Bibliographic record
Abstract
The Expert Library: Staffing, Sustaining, and Advancing the Academic Library in the 21st Century. Edited by Scott Walter and Karen Williams. Chicago, Illinois: Association of College and Research Libraries, 2010. 373 pp. $48 USD. ISBN-13: 978- 0-8389-8551-9. *The Expert Library, edited by Scott Walter, Associate University Librarian for Services and Associate Dean of Libraries at the University of Illinois at Urbana-Champaign and Karen Williams, Associate University Librarian for Academic Programs at the University of Minnesota, brings together the ideas and opinions on myriad issues facing a modern academic library. They do this by calling on twenty-one prestigious and well known authors, mainly academic librarians, from large research institutions in the United States to create thirteen distinct and well structured chapters. authors such as Marta Brunner, David Lankes, James Neal and Lisa Janicke Hinchcliffe are all from the US, they are well known internationally, and this book is very applicable to the Canadian academic library community.James Neal, from Columbia University, who spoke at the 2011 Ontario Library Association's Super Conference begins the book with a foreword setting the tone with a theme that is touched on in virtually every chapter, that of the need for staff, either current or future, to be diverse, flexible and adaptable. The first line in the Introduction indicates a long needed change in attitude for academic libraries that have focused, or sometimes perhaps over-focused, on acquisitions and collections: Though we sometimes forget to celebrate this fact, the library's most valuable collection is its people (ix). The book is then broken up into thirteen scholarly, yet exceptionally readable, chapters.Chapters 1 and 2 by David W. Lewis and John Lehner respectively, review the issues regarding current and future staffing. Lewis discusses trends in the incorporation of more IT staff working alongside librarians and library staff, painting a picture of what the academic library workforce might look like in five years. Lehner covers new approaches to the processes of selecting staff, including criteria for selection and behavioural interviewing. He recommends hiring committees move away from asking about acquired skills, but instead focus on investigating a potential employee's personal abilities, particularly flexibility, adaptability and creativity, by asking situational-type questions.R. David Lankes writes the third chapter which changes course a bit. It is entitled Innovators Wanted No Experience Necessary, and it is written with a sailing/nautical theme which doesn't match most of the rest of the book. Putting aside the awkward sailing metaphors, Lankes does an excellent job of describing participatory librarianship and the skills involved in making this concept successful. Chapters 4 6 focus on competencies. Heather Gendron describes the standards and competencies for academic libarianship, referring to documents such as ARL's core competencies SPEC kit. In Chapter 5 Craig Gibson and Jamie Coniglio give a clear, well written explanation of the rise of the liaison librarian and examine why the model is not perfect. They note that through astute recruitment of, or development of, talented, innovative, risk-taking staff with a research-and-development mind-set who are attuned to the large changes sweeping higher education rather than just to their particular functional role (p. 117) academic libraries can flourish. …
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.019 |
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".