MétaCan
Menu
Back to cohort
Record W2000741207 · doi:10.1002/meet.2009.1450460135

Diverse approaches to “tasks” in information science: Conceptual and methodological insights

2009· article· en· W2000741207 on OpenAlexaff
Eric M. Meyers, Theresa Dirndorfer Anderson, Katriina Byström, Luanne Freund, Elaine G. Toms

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2009
Typearticle
Languageen
FieldComputer Science
TopicInformation Retrieval and Search Behavior
Canadian institutionsDalhousie UniversityUniversity of British Columbia
Fundersnot available
KeywordsInformation seekingTask (project management)Perspective (graphical)Context (archaeology)Presentation (obstetrics)Computer scienceDiversity (politics)Cognitive models of information retrievalConceptual frameworkKnowledge managementData scienceSociologyWorld Wide WebHuman–computer information retrievalSocial scienceInformation retrievalArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Abstract The “task” is an important concept in Information Science, both as a theoretical and methodological tool. While many studies of information retrieval and information seeking and use take individual characteristics or system features as the starting point for their research, a growing body of work has focused on the socio‐cultural perspective. This approach examines the relationship between tasks and search processes, as well as information‐intensive task features and anticipated learning or work outcomes in a given context. This latter perspective has been utilized in the emerging work on collaborative information behavior, which recognizes the interplay of actors, environments, and task demands in understanding information seeking. Building on prior discussions of task‐oriented research, this panel of well‐known and emerging scholars from Australia, North America and Scandinavia will further explore how tasks may guide information seeking and retrieval theory and research. Panelists will present a balance of conceptual investigations, as well as recent empirical studies, to illustrate the wide‐array of issues and insights in this area. Of particular concern to this panel will be the role of diverse perspectives in understanding tasks: how cultural and contextual dimensions of user behavior condition the manner in which we conceptualize tasks, as well as how tasks are utilized in contextually‐sensitive information seeking and retrieval research. The strength of this panel is its diversity: in the spirit of the 2009 ASIS&T Annual Meeting theme, we will explore how, in a pluralistic society, no one presentation on “task” can truly encompass the concept. The topics covered will span life‐long (childhood through adult) as well as life‐wide (formal and informal) contexts of behavior. By bringing together an array of perspectives on this topic, we will foster a wide‐ranging discussion of theoretical and methodological issues surrounding task‐oriented research.

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)0.077
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.077
Threshold uncertainty score0.405

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0770.068
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0280.030
Science and technology studies0.0090.064
Scholarly communication0.0300.041
Open science0.0050.019
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0020.001

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 head0.101
GPT teacher head0.317
Teacher spread0.216 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the American Society for Information Science and TechnologySame topicInformation Retrieval and Search BehaviorFrench-language works237,207