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Record W1568749169

Information Sources Used by Lawyers in Problem-Solving: An Empirical Exploration

2001· article· en· W1568749169 on OpenAlexaff
Margaret Ann Wilkinson

Bibliographic record

VenueScholarship@Western (Western University) · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsWestern University
Fundersnot available
KeywordsInformation seekingWork (physics)Task (project management)Public relationsFocus (optics)PsychologyEmpirical researchPolitical scienceSocial psychologyComputer scienceManagementEngineeringInformation retrievalEconomicsEpistemology
DOInot available

Abstract

fetched live from OpenAlex

The information-seeking behavior of lawyers has not been fully investigated empirically. Prior work has tended to focus on legal research as the central task performed by lawyers in their information-seeking activities. This analysis of more than 150 interviews of practicing lawyers showed that legal research should not be considered information-seeking. The lawyers interviewed identified other tasks, such as administration of their law practices, as constituting problem-solving, information-seeking activities. In solving their problems, the lawyers overwhelmingly preferred informal sources when seeking information. In addition, they preferred sources of information internal to their organizations rather than external sources, although this was less true for lawyers from smaller firms. Neither the lawyer’s gender nor the size of the center in which the practice was located influenced the type of information sources chosen. The model for the information-seeking behavior of professionals advanced by another author group is discussed and modifications are suggested that create a new model offering a fuller picture of the behavior of lawyers.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.024
Open science0.0000.000
Research integrity0.0000.001
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 head0.187
GPT teacher head0.407
Teacher spread0.220 · 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.

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
Published2001
Admission routes1
Has abstractyes

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