Bibliographic record
Abstract
In today's world of new media and big data, our personal data is a valuable commodity. This Master's thesis presents a little-known industry of personal data brokers. Databases of US data brokers contain surprisingly detailed and sensitive information of millions of Americans. The thesis also contains an analysis of risks related to insufficient protection of personal information in digital economy along with possibilities how to enhance our digital privacy in connection with data brokers. The core of the thesis is a comparative analysis of data broker legislation in the US, Canada and the European Union. The analysis shows that in the US there is no unified regulation of personal data protection from activities of data brokers but several laws partially regulating some aspects of personal data protection; this system allows trade in personal data even without the acknowledgement of the persons. On the other hand, regulation in the EU and Canada favours protection of personal data and privacy. In the EU each member state has its legal act on personal data protection based on the EU directive. In April 2018 this directive will be replaced by General Data Protection Regulation which will be directly applicable in all member states. Both current and future legislation, however, make the data broker...
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".