NEXUS: A Synergistic Human-Service Ecosystems Approach
Bibliographic record
Abstract
This paper presents a novel approach to organizational effectiveness that challenges the static hierarchies predominant in traditional institutional frameworks. Human-Service Ecosystems (HSEs) represent individuals and the technologies they use to achieve goals within an organization. However, in situations involving complex dynamics, such as emergency response, organizations are often ineffective at achieving these goals. This ineffectiveness has been attributed, among other things, to the organization's underlying structure, as static hierarchies are ill-suited to addressing the dynamics of an unfolding situation, and to individuals' cognitive limitations. By improving human-machine effectiveness, while simultaneously improving the interaction between individuals, operationally-networked organizations can synergistically emerge through dynamic social structures supported by dynamic information systems. This paper proposes a new approach, NEXUS, which leads to the design of such an information system (based on the concept of HSE), capable of supporting both the dynamics necessary to match situational complexity, and the formal and informal social structures present within organizations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".