The nested blocks and guidelines model
Bibliographic record
Abstract
We propose the nested blocks and guidelines model for the design and validation of visualization systems. The nested blocks and guidelines model extends the previously proposed four-level nested model by adding finer grained structure within each level, providing explicit mechanisms to capture and discuss design decision rationale. Blocks are the outcomes of the design process at a specific level, and guidelines discuss relationships between these blocks. Blocks at the algorithm and technique levels describe design choices, as do data blocks at the abstraction level, whereas task abstraction blocks and domain situation blocks are identified as the outcome of the designer’s understanding of the requirements. In the nested blocks and guidelines model, there are two types of guidelines: within-level guidelines provide comparisons for blocks within the same level, while between-level guidelines provide mappings between adjacent levels of design. We analyze several recent articles using the nested blocks and guidelines model to provide concrete examples of how a researcher can use blocks and guidelines to describe and evaluate visualization research. We also discuss the nested blocks and guidelines model with respect to other design models to clarify its role in visualization design. Using the nested blocks and guidelines model, we pinpoint two implications for visualization evaluation. First, comparison of blocks at the domain level must occur implicitly downstream at the abstraction level; second, comparison between blocks must take into account both upstream assumptions and downstream requirements. Finally, we use the model to analyze two open problems: the need for mid-level task taxonomies to fill in the task blocks at the abstraction level and the need for more guidelines mapping between the algorithm and technique levels.
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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".