Time to Change: On Distributed Computing in Dynamic Networks (Keynote)
Bibliographic record
Abstract
In highly dynamic networks, topological changes are not anomalies but rather integral part of their nature. Such networks are becoming quite ubiquitous. They include systems where the entities are mobile and communicate without infrastructure (e.g. vehicles, satellites, robots, or pedestrian smartphones): the topology changes as the entities move. They also include systems, such as peer-to-peer networks, where the changes are caused by entities entering and leaving the system, They even include systems where there is no physical mobility at all, such as social networks. A vast literature on these dynamic networks has been produced in many different fields, including distributed computing. The several efforts to survey the status of the research and attempts to clarify and classify models and assumptions, have so far brought more valuable bibliographic data than order and clarity. Goal of this note is to ask questions that might bring author and readers to start to clarify some important research aspects and put some order in a sometimes confusing field. The focus here is entirely on distributed computing, specifically on its deterministic aspects.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".