Collaborative Intelligence in a Decentralized Environment CIDE
Bibliographic record
Abstract
Numerous companies frequently engage in collaborative cooperation to exchange intelligence rather than resorting to competing strategies to achieve higher levels of intelligence. \textit{Intelligence} is essential in determining \textit{collaborative intelligence} . Intelligence can be discerned by a range of behaviors and actions that go beyond the mere individual observable activity. Collaborative intelligence is often defined as the combined actions of individuals (such as humans and machines) working towards a common goal. It encompasses more than just behaviour and includes other crucial elements such as the sharing of intelligence. The ontological view concerns the system's understanding and representation of information, data, and the fundamental reality it aims to capture and manage. This paper aims to expand upon our previous research on Intelligence in decentralized environments. The methodology utilized in this paper involves employing an ontological view to derive intelligence, encompassing its various forms, such as data, information, and knowledge. The paper explores the fundamental significance of intelligence and semantic integration in supporting the collaborative intelligence framework. Semantic integration is crucial to establishing a shared understanding of individuals' data, information, and concepts. It is fundamental in facilitating effective communication, intelligence sharing, and decision-making within a collaborative environment.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".