Subject Liaison Versus Functional Library Service Models in Academic Libraries: A Narrative Review
Bibliographic record
Abstract
Objective: The primary objective of this article is to determine what is the available evidence in the library and information science literature on subject liaison library service model and the functional library service model in academic libraries in Australia, Canada, United Kingdom and United States of America. Methods: This article uses a narrative review methodology to examine the qualitative themes of both the subject liaison and the functional library service model in academic libraries in Australia, Canada, United Kingdom, and the United States of America from 2009-2024. Library and Information Science databases were searched using a “keyword search strategy” and the results were screened using inclusion and exclusion criteria. The themes were determined from the data extraction process for included research studies. Results: As reported using the PRISMA flowchart (figure 1), there were 141 records that were screened. After exclusion criteria were applied, 43 reports were assessed for eligibility to be included in this review. It was determined that 29 studies were included in this narrative review on subject liaison and functional academic library service models. The main themes identified in the literature were: academic libraries strategic alignment with affiliated institutions of higher education, restructuring of the academic library, team-based library services model (subject and functional librarian teams), relationship management, embedded librarianship, and new roles of liaison librarians. Conclusion: Academic libraries have increased pressure to better align with the strategic goals of the institution of higher education to which such libraries are affiliated. This alignment pressure often results in reorganization of academic libraries from a subject based library service model to a functional based library service model. The literature suggests that a highly effective service model is the blending of best practices from both the subject based library service model and the functional based library service model (hybrid model). This involves building teams comprising both subject liaison librarians and functional specialists. There obviously are advantages and disadvantages of both models and the most effective service delivery may depend on the individual academic library.
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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 | 0.022 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".