What Are We Talking about When We Talk about Digital Protectionism
Bibliographic record
Abstract
For almost a decade, executives, scholars, and trade diplomats have argued that filtering, censorship, localization requirements, and domestic regulations are distorting the cross-border information flows that underpin the internet. Herein I use process tracing to examine the state and implications of digital protectionism. I make five points: First, I note that digital protectionism differs from protectionism of goods and other services. Information is intangible, highly tradable, and some information is a public good. Secondly, I argue that it will not be easy to set international rules to limit digital protectionism without shared norms and definitions. Thirdly, the US, EU, and Canada have labeled other countries policies’ protectionist, yet their arguments and actions sometimes appear hypocritical. Fourth, I discuss the challenge of Chinese failure to follow key internet governance norms. China allegedly has used a wide range of cyber strategies, including distributed denial of service (DDoS) attacks (bombarding a web site with service requests) to censor information flows and impede online market access beyond its borders. WTO members have yet to discuss this issue and the threat it poses to trade norms and rules. Finally, I note that digital protectionism may be self-defeating. I then draw conclusions and make policy recommendations.
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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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.014 | 0.068 |
| Scholarly communication | 0.029 | 0.063 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.015 | 0.033 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".