MétaCan
Menu
Back to cohort
Record W2162938212 · doi:10.1002/meet.14504701215

A taxonomy of functional units for information use of scholarly journal articles

2010· article· en· W2162938212 on OpenAlexaff
Lei Zhang, Rick Kopak, Luanne Freund, Edie Rasmussen

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2010
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceTask (project management)Taxonomy (biology)Set (abstract data type)Reading (process)Information retrievalFunction (biology)Data scienceLinguisticsEngineering

Abstract

fetched live from OpenAlex

Abstract Today's readers of scholarly literature want to read more in less time. With this in mind, this study applies the idea of the functional unit to the use of digital documents. A functional unit is the smallest information unit with a distinct function within the Introduction, Methods, Results and Discussion components of scholarly journal articles. Through a review and analysis of the literature and validation through user surveys, this study identifies a set of common functional units and examines how they are related to different tasks requiring use of information in journal articles and how they are related to each other for a particular information use task. The findings, presented in the form of a taxonomy, suggest a close relationship between functional units and information use tasks, and furthermore among a set of functional units for a particular information use task. This taxonomy can be used in the design of an electronic journal reading system to support effective and efficient information use.

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)0.008
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.994
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.050
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0210.010
Science and technology studies0.0030.003
Scholarly communication0.0060.009
Open science0.0020.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.031
GPT teacher head0.215
Teacher spread0.184 · 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 designQualitative
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

Citations10
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the American Society for Information Science and TechnologySame topicLibrary Collection Development and Digital ResourcesFrench-language works237,207