Bibliographic record
Abstract
Collection budgets are an essential tool for building collections yet the amounts of allocations can ebb and flow over the years. Modifying the budget structure is an intimidating, exhausting exercise with administrative and political ramifications that affect the workload of collections librarians as well as the workflows in acquisitions departments. External and internal forces such as impending budget cuts and serials reviews, a new library system, new department heads, newly minted librarians’ learning curves, and the creation or demolition of big deals seem like roadblocks to a budget revision process. They can also be seized as opportunities to look at new models. Libraries get by with the allocations provided in any given year, but would it be better for the collections if the approach to allocations was more flexible from the beginning, more of a proactive allocation instead of reactive? At Binghamton University Libraries, the hiring of a new Head of Collection Development and migrating to a new library system necessitated collaborative conversations concerning structures and roles for the two departments. This paper presents scenarios and recommendations for determining when and how to collaboratively evaluate a legacy budget structure, redefine allocations, and review staff roles.
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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.059 | 0.025 |
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".