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Record W2081250994 · doi:10.1002/meet.14504901074

Third space as an information system and services intervention methodology for engaging the user's deepest levels of information need

2012· article· en· W2081250994 on OpenAlexaff
Carol Collier Kuhlthau, Charles Cole

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2012
Typearticle
Languageen
FieldComputer Science
TopicInformation Retrieval and Search Behavior
Canadian institutionsMcGill University
Fundersnot available
KeywordsOperationalizationCurriculumSpace (punctuation)Information spaceIntersection (aeronautics)Knowledge managementComputer scienceIntervention (counseling)Information systemMathematics educationPedagogyPsychologyEngineeringWorld Wide WebEpistemology

Abstract

fetched live from OpenAlex

Abstract The paper discusses overview principles of information system and services intervention strategies for students researching a school assignment, then tests these principles in a field study. The principles are based on Kuhlthau's ISP Model, Cole's theory of information need and Maniotes' concept of Third Space. The six‐stage ISP Model describes information barriers that arise for students researching a school assignment while they are exploring information in Stage 3; they must transition to a focus formulation in Stage 4, but information overload and other barriers frequently block their thinking. The theory of information need seeks to explain successful Stage 3‐to‐Stage 4 transition as the student engaging his or her ways of knowing, which will enable focus formulation. Third Space is an intersection zone between the school curriculum and the student's knowledge and ways of knowing, creating a dynamic conception of the learning space that involves the student's outside‐the‐classroom knowledge. A content analysis study illustrates a methodology for operationalizing and testing these concepts and principles in a naturalistic setting.

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)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.005
Scholarly communication0.0020.003
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.310
Teacher spread0.279 · 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 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

Citations4
Published2012
Admission routes1
Has abstractyes

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