BOARD #145: Forming a Pod: A Naval Architecture, Marine and Ocean Engineering Librarian Community of Practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it