Bibliographic record
Abstract
The ITU-T has defined the global Intelligent Network (IN) standard IN Capability Set 1 (refined) (CS-1R) which is now being deployed worldwide. Global IN standardization has progressed in the last several years to produce the ITU-T IN CS-2 Recommendations which includes enhanced capabilities from CS-1R. But is there a future for evolution of global LN standards such as IN CS-3? Concerns have been raised regarding the scope, timeliness, and value of global IN standards. The advent of the IN Forum and the role of national and regional standards bodies also poses threats to global IN standards, in terms of duplication of effort and misalignment of approaches. The telecommunications industry, and IN in particular, is moving towards global service delivery, inter-domain interworking, and deregulation and increased competition. Is there a need for global IN standards in this new environment? This paper highlights key capabilities defined in IN CS-2 and indicates its relevance to the deployment of intelligent networks worldwide. The paper describes steps taken by the ITU-T and the IN subworking group to address concerns related to international standards timeliness and relevance. Finally, the paper discusses a variety of approaches in which the ITU-T IN sub-working group can achieve harmonization of regional interworking requirements and meet the needs of a dynamic industry.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".