The Time Has Come... To Move Many Things: Inventorying and Preparing a Collection for Offsite Storage
Bibliographic record
Abstract
In the spring of 2019, the Montana State University (MSU) Library embarked on a large-scale inventory project that involved weeding and moving portions of their collection to an offsite storage facility within six months in order to create more student study space in the Library. The department primarily responsible for leading the project, Collections Access & Technical Services, the result of two departments merging, was also simultaneously navigating their new structure and a remodel of their workspace thus adding further challenges to the project. This poster session demonstrated how MSU Library approached and completed this project by advocating to their Library Administration for additional resources, including hiring a project manager and third-party companies to assist with the inventory and moving of the collection. It also discussed the types of work groups formed to identify new workflows (i.e., retrieval of offsite items) and modify existing ones, involving student employees in the project, and internal and external collaborations that took place. Additionally, presenters shared strategies used to communicate to their campus community, and the impact this project has had on our patrons. They also included statistics that were gathered during the project including deselection figures, the number of materials that did not have barcodes and were not accounted for in the Library’s catalog and discovery layer (Ex Libris’ Alma and Primo), and what subject areas currently remain in the main library building.
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.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.015 |
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".