Access My Info: measuring data access rights around the world
Bibliographic record
Abstract
In 2014, the Citizen Lab and Open Effect started Access My Info (AMI) a research project that uses data access requests and complementary policy, legal, and technical methods to learn about how private companies collect, retain, process, and disclose individuals’ personal data. Accompanying the research methodology is a web-based tool that helps members of the public generate data access requests based on templates tailored to different industries. AMI was first applied in Canada and resulted in tens of thousands of Canadians making DARs to telecommunication companies. The results of the study showed inconsistent responses across companies and documented consumers experiencing in significant barriers to accessing their data. Following the first AMI project in Canada, the Citizen Lab formed a working group to bring the research method to Asia and comparatively measure responses to DARs across the region. The working group includes academics, lawyers, advocates, and designers working in five jurisdictions: Hong Kong: Lokman Tsui (Chinese University of Hong Kong), Stuart Hargraves (Chinese University of Hong Kong), Keyboard Frontline (advocacy organization, Hong Kong), InMedia (media group, Hong Kong), Jason Li (Designer, Hong Kong) South Korea: Kelly Kim (OpenNet Korea), KS Park (Korea University) Australia: Adam Molnar (University of Waterloo / Deakin University) Indonesia: Sinta Dewi Rosadi (University of Padjadjaran) Malaysia: Sonny Zulhuda (International Islamic University Malaysia)
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".