Managing Variations in Meaning: Guidance for Using “Complexity” and Related Terms
Bibliographic record
Abstract
ABSTRACT The term “Complexity” is widely used across disciplines, where it often represents distinct but related concepts such as complicatedness, emergence, difficulty, uncertainty, and chaos. This variability in usage can create miscommunication and misunderstanding, even within structured organizations like the International Council on Systems Engineering (INCOSE). This paper addresses this challenge by offering guidance tailored to three primary audiences—General/Casual, Practitioner, and Research—on using and interpreting “Complexity” effectively across trans‐disciplinary contexts. Unlike efforts that prescribe a single definition, the approach here respects the variety of interpretations while providing techniques and ontologies to clarify usage. To illustrate, the paper compares different “Complexity” definitions, fostering awareness of both the similarities and distinctions. By promoting a common understanding, rather than a definition, this paper lays essential groundwork for future initiatives aimed at developing a unified scientific basis for “Complexity”, enabling clearer, more consistent communication, and application.
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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 | 0.087 | 0.227 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.010 | 0.054 |
| Scholarly communication | 0.026 | 0.056 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".