MétaCan
Menu
Back to cohort
Record W2154920863 · doi:10.1002/meet.14504901078

Crossing the divide: Putting information seeking research and theory into computer science practice to make information search systems and services more effective for the user

2012· article· en· W2154920863 on OpenAlexafffund
Carol Collier Kuhlthau, Donald O. Case, Brenda Dervin, Marcia J. Bates, Charles Cole, Karen Fisher

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsMcGill University
FundersUnited WaySocial Sciences and Humanities Research Council of CanadaUniversity of WashingtonMicrosoft ResearchBill and Melinda Gates FoundationNational Science Foundation
KeywordsInformation seekingOperationalizationComputer scienceInformation systemInformation behaviorDilemmaInformation sciencePersonal information managementInformation architectureKnowledge managementService (business)Group information managementWorld Wide WebInformation seeking behaviorModerationUser ResearchInformation needsManagement information systemsHuman–computer interactionInformation retrievalUser experience designEngineering

Abstract

fetched live from OpenAlex

Abstract With Carol Kuhlthau as moderator, we propose a panel of six information behavior researchers with diverse views on operationalizing findings and theoretical positions in information behavior/information seeking research for application in information system design and for re‐envisioning library and information services for technological information environments. Whereas computer‐science designed information systems and technological environments in libraries are designed for the user with an answer or at least the form of the answer firmly in mind, information seeking research is interested in the user with a complex information need who utilizes an information system or library service for knowledge construction and sense‐making. The dilemma is how to communicate information behavior/information seeking research and objectives to those who design the systems. The panelists propose different views on and solutions.

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 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)0.021
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0070.008
Scholarly communication0.0020.014
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 head0.024
GPT teacher head0.371
Teacher spread0.346 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the American Society for Information Science and TechnologySame topicKnowledge Management and SharingFrench-language works237,207