Bibliographic record
Abstract
In this article I will first argue that a {\em Service-Infrastructure Cycle} is fundamental to networking evolution. Networks are built to accommodate certain services at an expected scale. New applications and/or a significant increase in scale require a rethinking of network mechanisms which results in new deployments. Four decades-worth of iterations of this process have yielded the Internet as we know it today, a common and shared global networking infrastructure that delivers almost all services. I will further argue, using brief historical case studies, that success of network mechanism deployments often hinges on whether or not mechanism evolution follows the iterations of this Cycle. Many have observed that this network, the Internet, has become ossified and unable to change in response to new demands. In other words, after decades of operation, the Service-Infrastructure Cycle has become stuck. However, novel service requirements and scale increases continue to exert significant pressure on this ossified infrastructure. The result, I will conjecture, will be a fragmentation, the beginnings of which are evident today, that will ultimately fundamentally change the character of the network infrastructure. By ushering in a ManyNets world, this fragmentation will lubricate the Service-Infrastructure Cycle so that it can continue to govern the evolution of networking. I conclude this article with a brief discussion of the possible implications of this emerging ManyNets world on networking research.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.270 | 0.163 |
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".