Information Sources Used by Lawyers in Problem-Solving: An Empirical Exploration
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.024 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".