Business Librarian as Learning Facilitator in and out of the Classroom: Practices at NYU Shanghai
Bibliographic record
Abstract
In the evolving landscape of business higher education, business librarians play important roles in the assurance of learning. In the classroom settings, librarians might be invited to deliver one-shot course-embedded library workshops, but they are increasingly recognized as pivotal learning facilitators beyond classes. Business librarians can connect with community members by highlighting targeted resources, coordinating community events, building professional pathfinders, and providing personalized consultations. The article shares with peers the practice at NYU Shanghai Library on how to explore diverse ways as learning facilitators, increase the impact of fostering professional thinking, and enhance real-world business learning experiences. 在不断发展的商科高等教育领域，商科图书馆员在学习保障中发挥着重要作用。在课堂环境中，图书馆员可能会受邀嵌入课程提供单次培训，但他们越来越被认为是课堂之外重要的学习促进者。商科图书馆员通过强调重点资源、协调校内活动、创建学科资源导航和提供个性化的参考咨询服务，与校内用户建立联系。本文分享了上海纽约大学图书馆如何运用多个途径完成上述实践。这些实践展示了作为学习促进者，商科馆员如何探索多种方式，以增加在培养商科学生专业思维上的影响力，并增强他们的真实商业学习体验。
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".