Bridging Representational Gaps: The Role of Tension and Multimodal Tools
Bibliographic record
Abstract
The human capacity to address complex problems depends on the ability and willingness to effectively engage and problem-solve with individuals who hold different views from one’s own. Bridging representational gaps by creating a shared understanding of these complex problems is critically important because in its absence, collaborators cannot generate viable solutions and, therefore, risk making the problem worse. Yet it is unclear how the process of bridging these gaps occurs and whether the use of multimodal tools may be useful in the process. Thus, we adopt an abductive approach to examining how and why do multimodal tools influence bridging representational gaps. Audio, video, and survey data were collected from 102 individuals who participated in one of fourteen workshops. The participants in each workshop were placed in two teams with opposing representations of the same problem and led through a series of structured meaning-making and knowledge sharing exercises centered on the use of toy building blocks to facilitate communication among team members. The results of our study demonstrate that improved communication across knowledge boundaries can be achieved by using multimodal tools to reify concepts. In leveraging the use of visual metaphors in their explanations, participants were better able to recognize areas of divergence and dependence between teams. This recognition enabled participants to transcend conflicting problem representations by constructing a third solution of mutual benefit, integrating ideas through a process that acknowledges the inherent tensions of collaborator diversity while encouraging shared understanding and creative problem-solving. Implications, limitations, and future directions are discussed.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".