Series Editorial: Network Softwarization and Management
Bibliographic record
Abstract
This series focuses on softwarization, management, and their integration in communication networks and services. “Network Softwarization” advocates for network architectures that separate the software implementing network functions, protocols and services from the hardware running them. “Network Management” aims to integrate fault, configuration, accounting, performance, and security capabilities in the network and to support self-management features, integral automation, and autonomic capabilities, empowering the network with inbuilt cognition and intelligence. The critical role that software and Management are increasingly playing in telecommunications is enabling unprecedented levels of abstraction, disaggregation, operation, integration, robustness, optimization, intelligence, precision delivery, programmability and cost and complexity reduction of infrastructures and services. Such an approach is resulting in even greater attainment of non-functional characteristics (e.g., qualities of the operation of a network, rather than specific behaviors, integrability, interoperability, operational guarantees, deployability, auditability and control, reliability, adaptability, elasticity, effectiveness, extensibility, automation and autonomicity).
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 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".