Transborder data flow: Implications for information dissemination and policies between the U.S., Canada and Mexico. Sponsored by SIG IFP, III
Bibliographic record
Abstract
Abstract The convergence of computing and networking has affected the ways in which people live, work and learn; the way institutions operate; and raised new issues and challenges for governments. National and global initiatives have been implemented to address the blurring of boundaries ‐geographical and political‐caused by electronic data transfer over these global networks. The flow of electronic data and digital content across jurisdictional lines calls for novel ‐or updated‐ laws and regulatory solutions (e.g., data protection, privacy, content regulation, etc.). This panel offers a comparative analysis of national laws and policies regulating the transport of data across national boundaries, with a special focus on the implications for information dissemination and access. A series of panelists from a variety of backgrounds and perspectives will explore these issues and describe how these are framed and debated in the United States, Canada and Mexico. The discussion should both inform and entice the audience to reflect on and pursue these timely problems.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.000 |
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".