Navigating Representational Gaps: Traversing Construal Levels and Investing in Uncertainty
Bibliographic record
Abstract
ABSTRACT Representational gaps (rGaps), which refer to inconsistencies in definitions of a group's problem, are notoriously pernicious and enduring. Team cognition research has primarily focused on increasing similarity and sharedness among members. However, this emphasis is insufficient when an rGap is present, as teams must retain and integrate diverse, and often conflicting, perspectives even as they converge on a solution. In this study, we build new theory on how groups navigate rGaps by embedding incompatible problem definitions in a simulation and recording 23 groups completing the simulation to examine the navigation process from before members are aware that an rGap exists to implementing a concrete solution to a given task. Qualitative analysis revealed a three‐phase process (i.e., Realizing, Integrating, Aligning) of navigating rGaps in which groups traverse multiple construal levels (i.e., Concrete, Problem, Comprehensive). We then explored why some groups ceased progression through the process and how this influenced the representations in their final solution (i.e., neither, single, bifurcated, integrated). Notably, investing in disruptive and generative uncertainty was critical to facilitating progression, challenging the assumption that uncertainty should be reduced, rather than invested in, to avoid harming performance. Our findings yielded important insights for the rGaps, uncertainty, and construal level literatures.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".