Life Cycle Costs of Library Collections: Creation of Effective Performance and Cost Metrics for Library Resources
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
An important issue for research librarians is the life cycle cost of acquiring and maintaining a collection. While purchase costs are easy to identify, associated acquisition, cataloging, circulation, and maintenance expenses are difficult to measure and attribute to specific collections. This paper develops a methodology to determine the life cycle costs of collections based on readily available statistical data collected annually by the Association of Research Libraries (ARL). ARL cost data (e.g., salaries and wages, materials expenditures, and operating expenses) for a specific library are allocated to collections (e.g., manuscripts, serials, and microforms) based on the size of the collection and its relative space requirements. By aggregating allocated costs, total life cycle costs for a collection can be estimated. Results of this research indicate that life cycle costs of collections are many multiples of their purchase costs. Results further suggest that the life cycle costs of monograph collections overwhelm the costs of other collections in research libraries—the cost structure of a research library is largely driven by its monograph collection. These results should prove useful in efforts to control costs and improve performance in research libraries.
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 | 0.002 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.011 |
| Open science | 0.001 | 0.001 |
| 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