Analyzing problem framing in design teams: a systems mapping approach
Bibliographic record
Abstract
Abstract Early phases of the design process require designers to select into view elements of the problem that they deem important. This exploration process is commonly referred to as problem framing and is essential to solution generation. There have recently been calls in the literature for more precise representations of framing activity and how individual designers come to negotiate shared frames in team settings. This paper presents a novel research approach to understand design framing activity using a system thinking lens. Systems thinking is the way that we understand a system’s components and the interrelations to create interventions, which can be used to move the system outcomes in a more favorable direction. The proposed approach is based on the observation that systems as mental representations of the problem bear some similarity to frames as collections of concepts implicit in the designer’s cognition. Systems mapping – a common visualization tool used to facilitate systems thinking – could then be used to model external representations of framing, made explicit through speech, and sketches. We thus adapt systems mapping to develop a coding scheme to analyze verbal protocols of design activity to retrospectively represent framing activity. The coding scheme is applied on two distinct datasets. The resulting system maps are analyzed to highlight team problem frames, individual contributions, and how the framing activity evolves over time. This approach is well suited to visualize the framing activity that occurs in open-ended problem contexts, where designers are more focused on problem finding and analysis rather than concept generation and detailed design. Several future research avenues for which this approach could be used or extended, including using new computational methods, are presented.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".