Semantic Computing for Organizational Effectiveness: From Organization Theory to Practice through Semantics-Based Modelling
Bibliographic record
Abstract
A critical function of an organization is to foster the level of integration (coordination and cooperation) necessary to achieve its objectives. The need to coordinate and motivation to cooperate emerges from the myriad dependencies between an organization’s members and their work. Therefore, to reason about solutions to coordination and cooperation problems requires a robust representation that includes the underlying dependencies. We find that such a representation remains missing from formal organizational models, and we leverage semantics to bridge this gap. Drawing on well-established organizational research and our extensive fieldwork with one of North America’s largest municipalities, (1) we introduce an ontology, formalized in first-order logic, that operationalizes concepts like outcome, reward, and epistemic dependence, and their links to potential integration risks; and (2) present real-world applications of this ontology to analyze and support integration in complex government infrastructure projects. Our ontology is implemented and validated in both Z3 and OWL. Key features of our model include inferable dependencies, explainable coordination and cooperation risks, and actionable insights on how dependency structures within an organization can be altered to mitigate the risks. Conceptualizing real-world challenges like incentive misalignment, free-riding, and subgoal optimization in terms of dependency structures, our semantics-based approach represents a novel method for modelling and enhancing coordination and cooperation. Integrated within a decision-support system, our model may serve as an impactful aid for organizational design and effectiveness. More broadly, our approach underscores the transformative potential of semantics in deriving tangible, real-world value from existing organization theory.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".