TDFs and Trade : The U.S.-Canada Free-Trade Agreement from a Transborder Data Flow Perspective.
Bibliographic record
Abstract
This paper examines the potential impacts of the comprehensive free-trade agreement negotiated by the United States and Canada in 1987 on transborder data flow between the two countries. An introductory overview of transborder data flow (TDFs) is followed by a discussion of how TDFs have been dealt with by both the United States and Canada to date; a brief history of the most recent free-trade negotiations between the two countries; and an analysis of the foundations of the agreement on two levels that relate directly to TDFs--its possible impact on trade in services and its potential impact on foreign direct investment in Canada. The paper concludes with speculations about the long-term effects of free trade in the areas of trade in services and foreign direct investment as they pertain to TDFs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".