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Record W2010112799 · doi:10.1002/meet.1450430131

Information behavior realities in organizations

2006· article· en· W2010112799 on OpenAlexaff
Brian Detlor, Chun Wei Choo, Pierrette Bergeron, Lorna Heaton

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsUniversité de MontréalUniversity of TorontoMcMaster University
Fundersnot available
KeywordsGroup information managementKnowledge managementInformation behaviorPersonal information managementInformation systemOrganizational behavior managementInformation mappingPoint (geometry)BusinessManagement information systemsPsychologyOrganizational learningComputer scienceOrganizational behavior and human resourcesEngineeringHuman–computer interaction

Abstract

fetched live from OpenAlex

Abstract This short paper provides discussion on the influencing effect of information behavior in organizations, as well as the forces which influence information behavior itself. The research goal is to offer insight on the nature of information behavior in organizations. To do so, the authors present findings from their quantitative analysis of a Web‐based survey administered in one particular information knowledge and information intensive firm. Results point to: i) the stronger role information behavior plays over information management processes and policies in affecting organizational information use outcomes; ii) the influencing effect of the information environment on organizational information behaviors; and iii) the influencing effect of both the information environment and organizational information behaviors on personal information behavior.

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)0.004
metaresearch head score (Gemma)0.022
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.268
Teacher spread0.259 · 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

Citations2
Published2006
Admission routes1
Has abstractyes

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