Managing Variations in Meaning: Guidance for Using “Complexity” and Related Terms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it