Bibliographic record
Abstract
The Digital Ecosystem (DE) paradigm is a holistic management/design/integration approach that is based on the notion of self-interested, self-managing, proactive and autonomous digital entities that evolve and self-organize. This in turn leads to the emergence of complex, self-organizing behaviours within a DE due to the evolving and highly dynamic interactions of its members. These interactions are however strongly influenced by the IT environment in which the DE is situated. Interestingly, our IT environments are currently undergoing a major transformation due to the declining importance of the desktop computing model. As smartphones and tablets begin to replace the desktop as the primary means of interacting with IT resources, our IT infrastructures are adapted to serve the increasing numbers of resource-constrained, wirelessly connected mobile devices that interact with backend components hosted in a cloud. These changes of the IT environment have also a major impact on the way Digital Ecosystems can be designed and how they operate. This paper focusses on the communication challenges of mobile digital ecosystems and presents an event-oriented form of communication that ensures high-scalability and loose coupling.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".