The Shape(s) of Information Practice: Using Radial Mapping Qualitatively
Bibliographic record
Abstract
ABSTRACT Information practices comprise both seeking and avoidance. Although information practices scholars use qualitative and visual methods to understand seeking practices, they rarely do so to understand avoidance. This paper proposes a new visual method that supports revealing and explaining the complex interplay of seeking and avoidance in rich qualitative data. We introduce seven dimensions of information seeking and avoidance practice (intensity, granularity, engagement, control, relevance, quality, and timeliness). We conceptualize these visually as axes radiating outward from a central origin. We use Excel radar charts to depict these dimensions, allowing us to identify and characterize the shapes of information seeking and avoidance in everyday information practices. We apply our approach to two cases from published literature to show how mapping reveals the interplay between seeking and avoidance at one point in time and over time. We propose potential data collection and analysis applications of this method for information practices research.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.014 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".