Towards a Right to Privacy in Transnational Intelligence Networks
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
Privacy is one of the most critical liberal rights to come under pressure from transnational intelligence gathering. This Article explores the many ways in which transnational intelligence networks intrude upon privacy and considers some of the possible forms of legal redress. Part II lays bare the different types of transnational intelligence networks that exist today. Part III begins the analysis of the privacy problem by examining the national level, where, over the past forty years, a legal framework has been developed to promote the right to privacy in domestic intelligence gathering. Part IV turns to the privacy problem transnationally, when government agencies exchange intelligence across national borders. Part V invokes the cause cause célèbre of Maher Arar, a Canadian national, to illustrate the disastrous consequences of privacy breaches in this networked world of intelligence gathering. Acting upon inaccurate and misleading intelligence provided by the Canadian government, the United States wrongfully deported Arar to Syria, where he was tortured and held captive by the Syrian Military Intelligence Service for nearly one year.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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