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Record W2162938212 · doi:10.1002/meet.14504701215

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

2010· article· en· W2162938212 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

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
Scholarly communication0.0000.025
Open science0.0010.000
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 head0.031
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