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Record W2096561211 · doi:10.1177/0165551506068159

Working with information: information management and culture in a professional services organization

2006· article· en· W2096561211 on OpenAlexaffabout
Chun Wei Choo, Colin Furness, Scott Paquette, Herman A. van den Berg, Brian Detlor, Pierrette Bergeron, Lorna Heaton

Bibliographic record

VenueJournal of Information Science · 2006
Typearticle
Languageen
FieldComputer Science
TopicInformation Architecture and Usability
Canadian institutionsUniversité de MontréalMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsGroup information managementPersonal information managementInformation managementInformation systemBusinessOrganizational cultureKnowledge managementManagement information systemsInformation governanceInformation technology managementInformation technologyPublic relationsComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The paper presents a case study of a large Canadian law firm with a distinctive information culture that is vigorously implementing an information management strategy. Our findings suggest that, at least for this organization, information culture trumps information management in its impact on information use outcomes. Thus, the strongly held information values and behaviors in the firm accounted for more than one-third of the variance in information use outcomes. Employees did perceive a high level of information management activity in the firm, although information management played a smaller, perhaps indirect role in explaining information use outcomes. What might organizations do to improve information use? This study suggests that organizations might do well to recognize that, in the hustle and bustle to implement strategies and systems, information values and information culture will always have a defining influence on how people share and use information.

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)0.010
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.893

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0130.007
Scholarly communication0.0070.002
Open science0.0010.003
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 head0.004
GPT teacher head0.209
Teacher spread0.206 · 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

Citations118
Published2006
Admission routes2
Has abstractyes

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