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Record W2561297306 · doi:10.1002/pra2.2016.14505301052

Evolution of information practices over time

2016· article· en· W2561297306 on OpenAlexaffabout
Devon Greyson

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2016
Typearticle
Languageen
FieldComputer Science
TopicInformation Retrieval and Search Behavior
Canadian institutionsChild and Family Research InstituteUniversity of British Columbia
Fundersnot available
KeywordsAffect (linguistics)Context (archaeology)Grounded theoryNaturalismInformation behaviorNaturalistic observationEthnographyPsychologyPopulationQualitative researchSociologyDevelopmental psychologySocial psychologyComputer scienceEpistemologySocial scienceGeographyDemography

Abstract

fetched live from OpenAlex

ABSTRACT Although researchers have grappled with conceptualizations of time in relation to information behavior, the effect of time on information practices has been a challenge to study and theorize. Longitudinal naturalistic methods provide an opportunity to observe information practices in context over time, but have infrequently been used in information research. This paper presents a qualitative ethnographic exploration of the changes over time in the information practices of a group of young parents in Canada, a population experiencing substantial life changes as young adults and new parents both. Using grounded theory, this analysis explores time‐related processes in the lives of young parents and they ways these processes affect information practices such as seeking, sharing, and use of information. Three case examples illustrate the interplay over time of individual characteristics, setting, and events, and the impact on an individual's information practices. Based on these findings, a theoretical model to inform future investigations of information practice evolution over time is presented.

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)0.006
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.003
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.252
Teacher spread0.244 · 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 designObservational
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

Citations13
Published2016
Admission routes2
Has abstractyes

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