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Record W1975662401 · doi:10.1002/meet.1450390116

A question of quality: The effect of source quality on information seeking by women in IT professions

2002· article· en· W1975662401 on OpenAlexaff
Christine Marton, Chun Wei Choo

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsInformation source (mathematics)Competitor analysisQuality (philosophy)Information qualityInformation seekingData sourcePsychologyWorld Wide WebInformation systemComputer scienceBusinessMarketingEngineeringLibrary scienceInformation retrieval

Abstract

fetched live from OpenAlex

Abstract This paper presents preliminary results from a study of how women in information technology (IT) professions use a range of information sources in their day‐to‐day work activities. Through a questionnaire survey, the study investigates the effects of Perceived Source Accessibility and Perceived Source Quality on the selection and use of information sources. Thirteen information sources, including the World Wide Web and Web‐based computer‐mediated communication, were identified. Sixty‐seven participants completed the survey. The most frequently used information source is the World Wide Web, followed by mass media, colleagues in the same department, computer‐mediated communication, business professionals and associates, and colleagues in a different group/department. The least used information sources are the internal library, and competitors. For many of the sources, there was a strong relationship between perceived source quality and source usage. This finding runs counter to early, well‐known studies that concluded that scientists and engineers selected sources based only on their accessibility. Surprisingly, the present study did not find a significant relationship between source accessibility and source usage. The implications for research are discussed.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.624
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.020
GPT teacher head0.333
Teacher spread0.313 · 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 teacher head, 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

Citations31
Published2002
Admission routes1
Has abstractyes

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