The Snowden Archive-in-a-Box: A year of travelling experiments in outreach and education
Bibliographic record
Abstract
The Snowden Archive-in-a-Box is an offline wireless network and web server providing private access to a replica of the Snowden Digital Surveillance Archive. The online version is hosted by Canadian Journalists for Free Expression. A work-in-development since April 2015, the Archive-in-a-Box is both a research tool and a tool for public education on data surveillance. The original version is powered with battery packs and housed in a 1960s spy style briefcase. When it is turned on, anybody in the vicinity can access the archive by connecting their wireless device to the Snowden Archive WiFi network and browsing to a website. Open the briefcase up and one finds a wood panel with a flatscreen inset, playing back the IP traffic of the archive's current users. Thus, while an audience such as a class of students or workshop group can access the Snowden documents and learn about mass surveillance from primary materials, they are also shown what data surveillance ‘looks like’. This Commentary explores my experiences during the first year of the Snowden Archive-in-a-Box. I examine my experiences as an international traveller carrying a suspicious briefcase of Top Secret materials and this project's reception by certain audiences. The project is still a prototype, yet it is quickly gathering a following and a number of permanent installations around the world. What could this mean for the future of surveillance education and leaks-enabled research?
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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".