Embedded Librarianship is Not Well Understood by Librarians at Chinese Universities, but Represents a Promising Service Model
Bibliographic record
Abstract
A Review of: Sun, H., Liu, Y., Wang, Z., & Zuo, W. (2019). Embedded librarianship in China: Based on a survey of university libraries. The Library Quarterly, 89(1), 53–66. https://doi.org/10.1086/700663 Abstract Objective – To determine the extent to which embedded librarianship is understood and implemented with a focus on service models, best practices, and barriers. Design – Survey questionnaire with follow up interviews. Setting – Provincial and ministerial university libraries in China. Subjects – Subject or liaison librarians from the 84 institutions with science and technology “information searching and evaluation centres” called S&TNS (p. 56). Methods – The authors identified potential participants through the eligible institutions’ library websites or by contacting the library’s managers. Then they randomly selected three librarians (n = 252) from each library to be invited to participate. 56 responded from 41 unique institutions. When respondents indicated that their library had embedded library services, the authors contacted them for follow up interviews. Main results – Results of the questionnaire revealed that most respondents were unclear about the concept of embedded librarianship with many mistaking traditional models of librarianship as embedded. Roughly half (n = 21) of respondents reported embedded librarians at their institution. Follow up interviews revealed five models of embeddedness: (1) subject librarianship, (2) teaching information retrieval or library orientation sessions, (3) participation in research teams, (4) co-location with academic departments, and (5) assisting university administration with decision-making. Only half of these libraries (n = 11) conducted some form of assessment. Conclusion – Embedded librarianship is a promising, but not yet widely adopted model in Chinese university libraries. More should be done to advocate for its implementation or libraries risk obsolescence.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".