Bibliographic record
Abstract
Multi protocol label switching (MPLS) is a type of data traffic routing protocol for core and wireless internet networks which originated in the 1990s. Its usage has grown enormously in next generation networking as it is considered superior in many ways to traditional routing for its traffic control and engineering capabilities especially in 4G LTE wireless networks. This paper discusses ramifications of MPLS routing and deep packet inspection (DPI) in the growing area of end user privacy and policy based networking. DPI examines information not only in layers 2 through 4 of the Open Systems Interconnection (OSI) model, but also layers 5 through 7, as required. DPI performs an analysis of the packet in order to identify applications associated with the traffic. MPLS traffic is ‘typed’ at present into four to six classes of service (CoS) to denote the type of data but many more CoS classes are possible and here is where further differentiation of data traffic type and service are conceivable. As argued in this paper privacy concerns arise from the fact that the end user IP number, their type of device, geographic location and type of data traffic [or application in use at any particular point in time] are being aggregated together. Other policy based networking technologies have emerged such as software defined networking (SDN) OpenFlow; yet much of the detail in SDN still remains to be resolved. Privacy issues raised in this paper relate to SDN as many of the routing practices and usage of end user metadata are the same. The effort to provide end users with security for their ecommerce or other applications may require new forms of protection for end user privacy.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.015 |
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".