The iSquare protocol: combining research, art, and pedagogy through the draw-and-write technique
Bibliographic record
Abstract
This article introduces the iSquare protocol, a novel application of the draw-and-write technique. The protocol was developed in the field of information science to explore the visual dimension of information and as an alternative and complement to written definitions of information that dominate the literature. In addition to generating a new visual perspective on information, the approach has proven fruitful for artistic and pedagogical purposes. Here, the protocol is presented in detail for scholars within information science and those beyond who may adapt it to their own research questions. The article begins with an overview of the draw-and-write technique, followed by a history of its use in the iSquare Research Program. Then, the distinguishing features of the iSquare protocol, its artistic potentials and teaching applications are outlined. Links are provided to an instructional script and research instrument template, enabling turnkey implementation of the method.
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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 | 0.121 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.075 | 0.025 |
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".