On the Relationship Between Organizational Complexity and Organizational Structuration
Bibliographic record
Abstract
This article represents a contribution to the conceptualization of organizational complexity. The first part of the article relates the concept of complexity to the production tasks of the organization by deriving measures of the complexity of production and planning tasks within the organization. This move allows us to analyze organizational activities in terms of the computational complexity of the tasks that the organization carries out. Drawing on concepts from theoretical computer science, the article introduces a taxonomy of production tasks based on their computational complexity and shows how to use the notion of computational complexity to analyze organizational phenomena such as vertical integration disintegration, the choice between markets and organizations as performers of particular production tasks, and the internal partitioning of organizational tasks and activities. The article then relates the complexity of the production function of the organization to the ways in which organizations structure themselves. It attempts to bring theorizing about organizational behavior based on complexity theory closer to the conceptual realm of "mainstream" organization theory and to make the concepts of complexity theory more useful to empirical examinations of firm dynamics and organizational behavior.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".