Leveraging Use‐by‐Publication‐Age Data in Serials Collection Decisions
Bibliographic record
Abstract
Traditionally, usage figures for electronic serials have lumped all years of publication together. New tools give librarians information about usage according to the year of publication. They allow us to analyze the usage of current material separately from usage of content published in prior years. The relative value of current subscriptions and backfiles has important collection development implications. For example, many libraries subscribe directly to titles that are offered in aggregated databases, but with embargoes. The relative value of current content distinguished from prior years may be useful in reevaluating such subscription decisions. This paper discusses tools and techniques for analyzing usage by year of publication according to several measures—including COUNTER’s JR5 report, Google Analytics, ILL reports, and token reports, and discusses how librarians can use these tools to aid in decision‐making about serials collection development decisions.
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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 | 0.051 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.038 | 0.057 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".