An arts‐informed study of information using the draw‐and‐write technique
Bibliographic record
Abstract
There are untold conceptions of information in information science, and yet the nature of information remains obscure and contested. This article contributes something new to the conversation as the first arts‐informed, visual, empirical study of information utilizing the draw‐and‐write technique. To approach the concept of information afresh, graduate students at a North American iSchool were asked to respond to the question “What is information?” by drawing on a 4‐ by 4‐inch piece of paper, called an iSquare. One hundred thirty‐seven iSquares were produced and then analyzed using compositional interpretation combined with a theoretical framework of graphic representations. The findings indicate how students visualize information, what was drawn, and associations between the iSquares and prior renderings of information based on words. In the iSquares, information appears most often as pictures of people, artifacts, landscapes, and patterns. There are also many link diagrams, grouping diagrams, symbols, and written text, each with distinct qualities. Methodological reflections address the relationship between visual and textual data, and the sample for the study is critiqued. A discussion presents new directions for theory and research on information, namely, the iSquares as a thinking tool, visual stories of information, and the contradictions of information. Ideas are also provided on the use of arts‐informed, visual methods and the draw‐and‐write technique in the classroom.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".