Virtual Roadblocks: The Securitisation of the Information Superhighway
Bibliographic record
Abstract
The Internet we know today is both content filtered and packet shaped. Subsequently, it is not the free operating zone of meta-space early proponents expected. Contrary to conventional wisdom, a multitude of actors have shown an increased willingness to intervene and control communication via the Internet with precision and effectiveness. This paper employs the Copenhagen School’s conceptualisation of securitisation at the macro level to address the issue of global Internet filtering from a “network” position between traditional “national” security and critical “individual” security. It looks at the ways in which intervention into the Internet’s infrastructure is leveraged for governance through various research programs such as Ronald Deibert’s Open Net Initiative, which probes all aspects of a national information infrastructure over the long term, concluding that the scope, scale, and sophistication of global Internet filtering are increasing in non-transparent fashions. It should come as no surprise that since its dissemination, authoritarian regimes such as China, Iran and, Saudi Arabia have actively engaged in Internet filtering practices. What is troublesome is that advanced industrialised countries including Canada, Germany, and the United States have also followed suit. Reasons for doing so include: the securitisation of information communication after 9/11, to restricting access to material involving the sexual exploitation of children as well as ‘extremist’ websites. Considering these securitising moves, this paper argues that the more that filtering practices are withheld from public scrutiny and accountability, the more temping it is for framing authorities to employ these tools for illegitimate reasons such as the stifling of both opposition and civil society networks. Furthermore, due to increased connectivity, transparent Internet requires desecuritisation of social agents and international security structures in order to ensure more free information.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".