Out of the Loop: Enhancing Documentation and Transparency in Collaborative Causal Loop Diagrams to Capture Multiple Perspectives
Bibliographic record
Abstract
Collaborative causal loop diagrams (C-CLDs) help decision makers to model complex systems and processes, but existing tools offer little support for documenting the model-building process or capturing the provenance of stakeholder contributions. In this paper, we map the C-CLD design space to derive concrete requirements for documentation and transparency. We introduce Perspectiva, an interactive prototype shaped by those requirements and refined through iterative feedback. Perspectiva enables side-by-side navigation and comparison of CLDs, codifies changes and conflicting relationships, and preserves term provenance and contributor attri bution. Its core features include anchored nodes for topological consistency, node interaction, hover-activated provenance pop-ups, and colour-coded encodings. In user studies with domain and visualisation experts, participants reported that Perspectiva improved navigation, comparison, and provenance tracking relative to static diagrams as well as highlighting opportunities for enhancement and future research.
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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 | 0.017 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".