Son Mis Datos: Building Personal Data Literacies through Citizen Data Audits
Bibliographic record
Abstract
This paper reports the results of a pilot projct to build functional, reflexive, critical and tactical personal data literacies through citizen audits of corporate data practices in the digital economy. The project, called Son Mis Datos (It's My Data), leveraged the personal data access provisions of Peru's national data protection law to help participants access the personal data that private companies hold about them. The work was completed in collaboration with the Peruvian digital rights advocate HiperDerecho which built a website called http://www.SonMisDatos.pe to facilitate personal data requests. Once participants had requested their data, they were invited to conduct citizen data audits of corporate data practices. The project found that citizen data audits are an effective tool for building personal data literacies, and they also show promise for supporting a more engaged citizenry equipped to build a more citizen-centric data economy. However, the project also found that in order to realize this promise, it is important to confront the liberal and individualistic tendencies of privacy frameworks, and find ways to anchor both citizen data audits and personal data literacies in the criteria and local cognitive processes of the affected communities.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| 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".