What Are We Talking about When We Talk about Digital Protectionism?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it