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Record W2119329444 · doi:10.4033/iee.2012.5b.14.f

The value of scholarly reading in the life sciences

2012· article· en· W2119329444 on OpenAlexvenueno aff
Carol Tenopir, Rachel Volentine

Bibliographic record

VenueIdeas in Ecology and Evolution · 2012
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsnot available
FundersJoint Information Systems CommitteeUniversity of East AngliaImperial College LondonCranfield UniversityUniversity of DundeeDurham UniversityInstitute of Museum and Library Services
KeywordsReading (process)Value (mathematics)Library scienceScholarly communicationDisciplineAcademic librarySociologyMedia studiesPublic relationsSocial sciencePublishingPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Surveys of academic staff in six universities in the U.K. provide insights for publishers and universities into scholarly article, book, and other publication reading patterns of academics and differences based on academic discipline of readers. These surveys were part of the 2011 UK Scholarly Reading and the Value of the Library Study funded by JISC Collections and based on Tenopir and King Studies conducted since 1977. Reading patterns of life and environmental scientists differ from other disciplines, in particular social sciences. Scholarly articles, especially those obtained from the library’s e-journal collections, are vital to the work of all academic disciplines. Life and environmental scient-ists come into contact with multiple sources of information every day, including social media, and the biggest limitation scientists describe when it comes to finding and obtaining articles is cost and time. Knowing more about academic reading patterns help publishers and librarians design more effective journal systems and services now and into the future.

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 imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.201
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.990
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.201
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.007
Science and technology studies0.0030.006
Scholarly communication0.0100.008
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.061
GPT teacher head0.354
Teacher spread0.293 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2012
Admission routes1
Has abstractyes

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