Data is different, and that’s why the world needs a new approach to governing cross-border data flows
Bibliographic record
Abstract
Purpose Companies, governments and individuals are using data to create new services such as apps, artificial intelligence (AI) and the Internet of Things (IoT). These data-driven services rely on large pools of data and a relatively unhindered flow of data across borders (few market access or governance barriers). The current approach to governing cross-border data flows through trade agreements and has not led to binding, universal or interoperable rules governing the use of data. The purpose of this article is to explain the new role of data in trade and to explain why data in trade is different from trade in other goods and services. We then suggest a new approach at the national and international levels. Design/methodology/approach The author uses a mixed methods approach to examine what the literature says about data as a traded good and or service, examines metaphors regarding the role of data in the economy, and then examines whether or not data is really “traded.” Findings Many countries do not know how to regulate data driven services. There is no consensus on what the appropriate regulatory environment looks like, nor is there a consensus on what are the barriers to cross-border data flows and what constitutes legitimate domestic regulation. Originality/value This is the first article to explain both the unique nature of data and the ineffectiveness of the trade system to address that distinctiveness.
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 | 0.034 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.079 |
| Scholarly communication | 0.023 | 0.035 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.010 | 0.016 |
| 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".