The Nuts and Bolts of Supporting Change and Transformation for Research Librarians
Bibliographic record
Abstract
Libraries have a rich tradition of providing services and support to researchers.In recent years, changing technology, evolving research methods and requirements, and the transforming landscape of scholarly communication have revealed a need for libraries to actively engage scholars and participate in the entire research lifecycle.As liaison and subject librarian roles shift to a more holistic and engagement-focused model, it is important that libraries provide them with the tools and resources to develop new skills.This paper will focus on three ways in which the North Carolina State University Libraries created and supported relevant training and opportunities for research librarians to gain the expertise necessary to embrace new roles and deeper collaboration across the research enterprise.Examples include the Data and Visualization Institute for Librarians, the Visualization Discussion Series, and the Research Data Committee.Through these examples, we will share ideas for creating peer-to-peer learning opportunities, explore some of the skills necessary for increased engagement, and provide insights into the challenges and opportunities related to supporting and developing new skills for librarians.
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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 | 0.143 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.037 | 0.108 |
| Scholarly communication | 0.077 | 0.090 |
| Open science | 0.007 | 0.056 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".