Library Collection Development for Professional Programs: Trends and Best Practices
Bibliographic record
Abstract
Library Collection Development for Professional Programs: Trends and Best Practices. Edited by Sara Holder. McGill University: IGI Global, 2013. 478 p. $175 hardcover (ISBN: 978-1-4666-1897-8). Librarians are inherently disadvantaged in collecting for professional as they often approach this responsibility as an outsider. Standard selection tools (Resources for College Libraries, Books in Print, Choice, Ulrich's) largely ignore materials that support these programs, such as technical reports, digital image databases, government documents, sacred literature, conference proceedings, theses and dissertations, and textbooks. Furthermore, library materials for professional include both core titles in the discipline as well as very current materials for certification or testing requirements. Recent collection management texts cover many of the issues contained herein (conspectus method, deselection, collection development policies), but tend to be light on selection tools for the librarian charged with building in these areas. Acquisitions Librarian (now Journal of Electronic Resources Librarianship) published a series of subject-specific collection development articles in 2004, which were incorporated into the monograph, Selecting Materials for Library Collections (Haworth Information, 2004). Although the theoretical content holds and selection tools are still used, some descriptions in this book are outdated. The chapter on nursing refers to the Brandon-Hill lists and E-streams for nursing, neither of which is being maintained. The ALCTS Sudden Selector's guides are limited in subject coverage, to date addressing biology, chemistry, business, and communications. Library Collection Development for Professional Programs, therefore, is a welcome guide for students of library and information science, new librarians, or those with new selection responsibilities for professional programs. The disciplines profiled include both undergraduate and postgraduate programs: business, design, teacher education, engineering, nursing and allied health, law, library science, theology, and veterinary medicine. The chapter on bioinformatics outlines the process of developing collection guidelines for an evolving discipline, and can be applied to any emerging area of study. Interdisciplinary studies is included, as these have proliferated in recent years and are now career-oriented and ... structured similarly to professional programs (164). A book of contributed chapters can be uneven in content and suffer from repetition, especially in discussion of the common themes of budgets, marketing, deselection, and collection development policies. While there is some necessary overlap, these fundamental topics are covered in various depths and address needs specific to the discipline. Several touch on the importance of deselection as part of collection development, and the text also includes two chapters of case studies on weeding projects that are applicable to any discipline. The meat of each chapter, however, are the selection tools. Most chapters provide lists of core books, journals, and databases in the discipline, as well as resources for identifying additional titles from accrediting agencies, professional societies, discipline-specific publishers, review services, and electronic discussion lists. Some chapter authors provide lists of relevant Library of Congress call number ranges to assist collectors in identifying related materials in cross-disciplinary topics. Free and open access sources are included. All chapters are written by practicing librarians, and chapters progress from the broad to the specific. …
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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 | 0.052 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.024 | 0.076 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.041 | 0.034 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.022 |
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".