Internets: The changing role of Internet Protocols in evolving broadband technologies
Bibliographic record
Abstract
This study, drawing from Langdon Winner’s theory, which identifies the ways in which technology and infrastructure have the embedded politics of their designers, asks questions related to the power of the transport layer of the internet’s infrastructure. I use a mixed methods approach to study the transport layer including media history, primary document analysis, and utilize data derived from a network protocol reader called Wireshark. The findings show that traditional scholarly framings of the transport layer of the internet dubbed as a set of ‘dumb pipes,’ passive, and everything interesting happening at the internet’s edges (Lessig, 2006; Pickard & Berman, 2019), may soon be out of date following the introduction of ManyNets by Chinese corporation, Huawei from 2018-2020, through an introduction for a New Internet Protocol (New IP). I challenge the concept of ManyNets with ‘internets’ as a historic analysis of the development of the transport layer of internet infrastructure shows a pattern in this concept of multiple internets, opposed to the newly introduced ManyNets. As this study finds, developments in the transport layer have been changing due to the ways citizens use the internet (e.g., shifts from text-based platforms to live-streamed content). This study shows that the transport layer of the internet’s infrastructure is a growing politicized space in constant flux.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".