The GIST Gift & Deselction Manager: Redesigning Gift and Weeding Workflow in the Library
Bibliographic record
Abstract
Gifts and weeding are two of the hardest jobs librarians face in an academic library.Trying to decide what is worth keeping versus what should be weeded is made especially difficult when you face major constraints: space, time, labor, and costs.Current workflows may or may not work and are dependent on your staffing, library priorities and the goals of your collection development policy.SUNY Geneseo's Milne Library created a free open-source and innovative tool called the GIST Gift & Deselection Manager, designed to manage a new workflow for the time-consuming gifts and weeding process.For gift workflows, the GDM uses several APIs (Application Programming Interface) to return a list of local and consortia holdings; creates automated "Keep" or "Do not keep" recommendations based on a customizable subject conspectus; imports library-enriched data such as award-winners or core title lists for effective decision-making; allows staff to route gift items to reviewers for analysis; provides a customizable donor acknowledgment letter and even more.For weeding & deselection workflows, the GDM uses the same APIs to return a list of consortia holdings and full-text availability from HathiTrust and Google Books; makes "Keep" or "Do not keep" recommendations based on holdings and full-text availability, conspectus data and weight of item; and allows for major weeding projects using a batch import process with OCLC or ISBN numbers.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.038 |
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".