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Record W2532310306 · doi:10.5703/1288284316271

Leveraging Use‐by‐Publication‐Age Data in Serials Collection Decisions

2016· article· en· W2532310306 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsPurdue Pharma (Canada)
Fundersnot available
KeywordsComputer scienceValue (mathematics)Data collectionWorld Wide WebData scienceInformation retrievalStatistics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.785
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.007
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.098
GPT teacher head0.267
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it