Bibliographic record
Abstract
We present a simple conceptual model of what constitutes complexity and simplicity in design engineering. At the core of the model are the three concepts of 1) scale (how many things are there), 2) diversity (how many different kinds of things are there), and 3) connectivity (how many relationships are there between things). The model distinguishes essential from accidental complexity (i.e., the complexity that we, engineers, add while designing), and intrinsic versus perceived complexity. The model also articulates the complexity of the thing (or system) we design or observe versus the complexity of the community around the system: its users, designers, manufacturers, sellers, other systems, etc. This model is then used to articulate a set of heuristics to address complexity: reduce, hide, shrink, organize, explain, expose.. Finally we open the toolkits of engineers in various disciplines to identify strategies, methods, or tools that they can use to address complexity: design principles, guidelines, design methods, patterns, tactics, frameworks, etc. Approaches such as modeling, abstraction, partitioning can then be described in terms of our key concepts and heuristics; e.g., “abstraction reduces perceived complexity”. This conceptual model helps engineering students to better reflect on their practices of design, and how these practices vary across disciplines. It also provides a more systematic approach to answering the never ending question: “how can you make this simpler?”
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".