BOARD #145: Forming a Pod: A Naval Architecture, Marine and Ocean Engineering Librarian Community of Practice
Bibliographic record
Abstract
Naval Architecture, Marine, and Ocean Engineering (NAMOE) programs are unique in that they are specialized, interdisciplinary, and uncommon at both the undergraduate and graduate levels. As a result, librarians or subject specialists who liaise with these areas can encounter a lack of resources and knowledge to support the students and faculty in these programs. A group of librarians who have NAMOE programs as part of their institutions recently started a dedicated group, combining elements of communities of practice and peer group mentoring to discuss how best to support these programs and each other as professionals with varying experience in this subject area. Plans include the development of a resource similar to chapters in Osif’s Using the Engineering Literature, a crucial source for librarians supporting engineering disciplines that lists a comprehensive, discipline-specific suite of key resources, and enhancing discovery of OER in NAMOE. In this work-in-progress article, in addition to sketching out some of the resources we plan to create and share, we will discuss the formation of this group and reflect on how it has impacted our work. By combining our efforts, we will enhance teaching and research for NAMOE programs, deepen our expertise in NAMOE library services, and present a framework for other specialized librarian communities to follow.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.104 | 0.035 |
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".