Outsourcing and transborder data flows: the challenge of protecting personal information under the shadow of the USA Patriot Act
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
Governments are increasingly outsourcing service provision to private contractors in an effort to realize cost efficiencies. The passage of the USA Patriot Act, however, has caused concern that government outsourcing of data management to US-based companies could result in the violation of fundamental civil liberties. What follows is a case study of a Canadian provincial government's plan to out-source the administration of a public health insurance and drug plan to a Canadian subsidiary of an American company. Within the context of the larger international concern about the reach of the USA Patriot Act, the article discusses the Canadian response to the fear that outsourcing will compromise the security of personal health information. It concludes that while different privacy protection experts worldwide have drawn different conclusions as to the implications of the USA Patriot Act, the ability of governments to protect the large amounts of data that are entrusted to them is becoming increasingly difficult. Points for practitioners Globalization and electronic communication not only challenge the sovereignty of the nation-state, but complicate the environment that both companies and governments `do business' in. This is particularly true given the swift passage of the USA Patriot Act 45 days after the September 11 attacks on New York's twin towers. This study of public sector data management outsourcing demonstrates that accountability, transparency and control over governments and their agents must not be compromised in the face of high profile demands to enhance national security or due to more mundane pressure to increase administrative efficiency.
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 | 0.007 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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