Information triangulation: A complex and agentic everyday information practice
Bibliographic record
Abstract
In contemporary urban settings, information seekers may face challenges assessing and making use of the large quantity of information to which they have access. Such challenges may be particularly acute when laypeople are considering specialized or technical information pertaining to topics over which knowledge is contested. Within a constructivist grounded theory study of the health information practices of 39 young parents in urban Canada, a complex practice of information triangulation was observed. Triangulation comprised an iterative process of seeking, assessment, and sense‐making, and typically resulted in a decision or action. This paper examines the emergent concept of information triangulation in everyday life, using data from the young parent study. Triangulation processes in this study could be classified as one of four types, and functioned as an exercise of agency in the face of structures of expertise and exclusion. Although triangulation has long been described and discussed as a practice among scientific researchers wishing to validate and enrich their data, it has rarely been identified as an everyday practice in information behavior research. Future investigations should consider the use of information triangulation for other types of information, including by other populations and in other areas of contested knowledge.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.135 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.026 | 0.060 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".