Security and the Economy: The North American Computer and Communication Infrastructure - Canadian Speaker
Bibliographic record
Abstract
SpeakerMy presentation falls very well onto Theo's presentation.One of the reasons we wanted him to speak first is that he has given you a perspective of what the Internet and telecommunications look like.I want to speak to some of the more nitty, gritty legal issues that come out of it and the economic concerns the telecommunications industry has.When I speak about the telecommunications industry, I think it is fair to say that my views represent a consensus.I work for the American AT&T Corp, which is expanding slowly but surely globally.We are building a new network throughout the world to support our global customers, particularly multinational corporations.As we look at cyber terrorism and we look at security in the context of the telecommunications industry, there are some significant hurdles, as you can tell by the graphic depiction of what a network looks like and what an Internet Service Provider's (ISP) network would be.The industry has been working on a position paper.This includes the International Chamber of Commerce, the International Telecommunications User Group, the Union of Industrial and Employers Confederations of Europe, as well as the largest Internet service provider group in Europe, not to mention we also have in Canada the Information Technology Association, and other similar groups.There is tremendous concern about balancing the need to modernize legislation with what is technologically feasible, and the I Selma M. Lussenburg is Chief Regional Counsel for Canada and Vice President, Legal Affairs and General Counsel for AT&T's Canadian operations, a position she has held since 1998.She
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".