No-go zones: Ethical geographies of the surveillance industry
Bibliographic record
Abstract
In an industry as opaque as the surveillance technology industry, any effort to put in place safeguards to prevent human rights abuses using these technologies should be recognised and encouraged. But what happens when those systems fail? For surveillance technology companies, deciding where not to sell in a world full of eager government clients has important ethical and financial implications. The surveillance industry favours a country-agnostic framework that hews to sanctions and export laws. Advocacy and media groups argue to extend the no-sell zone beyond sanctioned governments to ‘authoritarian’ ones. Yet legal compliance is not the only factor influencing surveillance companies’ choices, this article argues. Based on original investigation, this article examines the social responsibility policies of communications surveillance technology vendors and the legal, reputational and normative concerns these demonstrate. The article explores the use of country rankings related to ‘authoritarianism’ and ‘good governance’ by examining the inner workings of a specific company in crisis, Procera Networks. As the cases featured demonstrate, closer attention to be paid processes of corporate responsibility norm-making within companies.
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.
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.049 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".