I Spy with My Little Eye: The power of visual facilitation in participatory processes
Bibliographic record
Abstract
Observations of an illustrator and a landscape architect This paper aims to investigate how visual techniques can improve collaboration and foster participatory design processes, ultimately leading to sustainable change. The authors propose that utilising various visualisation techniques can lead to improved solutions for future challenges that require collaboration across different fields. Drawing on their combined 20 years of experience, one as an illustrator and the other as a landscape architect, they emphasise the benefits of visual facilitation that stem from their personal experience and preference for visual thinking and making sketch notes. By exploring the cognitive aspects and the potentials found in visual thinking, the authors aim to shed light on the valuable contributions of visualisation in facilitating effective collaboration and driving innovation in multidisciplinary contexts. The authors have chosen graphic recording and gigamapping as two co-creating methods to discuss the benefits and drawbacks of these visual methods and provide a deeper understanding of how visualisations can enrich systems oriented design processes. Using these two examples, they want to motivate system oriented designers not only to enrich their processes with visualisations but also to recognise the strong influence of such methods in co-designing processes to promote collective idea generation. This paper will explore the extent to which visual thinking techniques can support collaborative and participatory processes in systems oriented design.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".