Bibliographic record
Abstract
There has been a significant shift in the design community for the last ten years. The world has become more complex, more stakeholders and interdisciplinary teams need to be consulted and involved through the participatory design processes. In the fields of service design and systems ergonomics, several systems mapping methods have been employed to visualise the complex interactions of the systems within systems. The system maps are often shared not only within the interdisciplinary design team, but also with external stakeholders who may not have been involved in initial map creation and discussion stage. Therefore, it is very important to create easily understandable system maps and present them in an ‘easy to use’ manner, but there exists little research on how to create and present complex and multi-layered system maps. The majority of research is based on single layer diagrams. Sevaldson (2011) took into account how a multi-layered diagram could be used to represent the systems within the systems, but the usability of diagrams was not considered. \nOn the other hand, newly introduced interactive mapping and presentation tools such as Prezi, Adobe Edge Animate and MapsAlive, could enable us to create diagrams and maps more easily interactive, e.g. hyperlinking, zooming in/out. This development also allows us to create narratives and contexts that have previously been hard to do. There is a great potential to explore how these new tools could be used to improve the usability of complex systems diagrams. Therefore, the aim of this study is to investigate how much an interactive, multi-layered zoomable map allowed users to more quickly understand, use and explore a complex system map compared to a static and single-layered map.
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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".