Translating Domain Expertise through Visual Sensemaking.
Bibliographic record
Abstract
Visual (graphic) designers lead their work with the creation of artifacts for presentation and dissemination of concepts, information and marketing propositions. Their process is governed by a dialectic between sensemaking and strangemaking methods that facilitates their understanding of a problem space. Visual thinking-models developed first as sketches, facilitate the creation of final, carefully rendered artifacts. The aim of this paper is to expose and mine these processes and techniques for their deeper sensemaking utility. For my case study I chose to focus on the outcomes from research that was conducted by the Alzheimer Society of Ontario and their partners. Their research was designed to engage various stakeholders in the creation of visualizations that capture the essential features of the “dementia journey”. The resulting visual metaphors were then critically examined and restructured by employing my visual design expertise, visual design principles and with reflection on participant response in semi-structured interviews. The new visual interpretation was developed through both a sensemaking and strangemaking lens that inform final illustrations. The subjective techniques a visual designer uses to create artifacts can be loosely correlated to objective visual design principles, thereby combining the visual novelty and impact of strangemaking, as in making the familiar highly differentiated, with the convergence on shared meaning of sensemaking. Nigel Cross (1982) formally describes this as “designerly ways of knowing”. I conclude that the visual thinking process, as a subset of a strangemaking mind-set, has valuable and under-utilized sensemaking features that aid in the comprehension of a problem space and clear the way for creative discovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".