“You Need to Make it as Easy as Possible for Me”: Creating Scholarly Communication Reports for Liaison Librarians
Bibliographic record
Abstract
INTRODUCTION The typical trifecta of liaison librarian positions (collections, reference, and teaching) is shifting to include additional skillsets and competencies, particularly scholarly communications. While liaison librarians adapt to these changing roles, the question of how to upskill and train liaison librarians in scholarly communications is timely and still in flux. The lack of time required to improve these competencies and skills is an oft-cited challenge. DESCRIPTION OF PROJECT To address the challenge of lack of time, this article describes a pilot project undertaken with the aid of a Master of Information Studies practicum student to create scholarly communications reports for liaison librarians. These reports provide background knowledge and discipline-specific information about the scholarly communications landscape, particularly within the institutional context. The goal of the reports is to provide liaison librarians with greater contextual knowledge of their disciplines and the publishing patterns within their departments. This article will discuss the methodology behind creating these reports as well as feedback from liaison librarians on their relevance and potential use. NEXT STEPS The initial pilot was promising, however using a practicum student to create such reports may not be sustainable. Other possibilities include holding “research report retreats” for liaisonlibrarians to complete their own reports with a scholarly communications expert on hand. Additionally, institutions without a master’s program in library and information studies could consider the creation and updating of such reports as a backup project for existing fulltime or student staff.
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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 | 0.036 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".