Journalistic Purposes and Private Sector Data Protection Legislation: Blogs, Tweets, and Information Maps
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper explores how changes in the ways in which information is consumed and disseminated by myriad individuals in myriad forms may impact data protection law in Canada. The author uses examples of blogs, Twitter and information maps to illustrate the problems which will inevitably arise when trying to discern which individuals and which information will properly fit into the journalistic purposes exception in Canadian data protection statutes. She suggests that exceptions for the collection, use or disclosure of personal information for journalistic purposes raise vital questions pertaining to the purpose and scope of these exceptions. Recent case law serves to illustrate the difficulties faced by decision-makers in defining the scope of these exceptions, particularly given the need to balance the public right to be informed with individual privacy rights. The author considers the journalistic purposes exceptions in light of the role of journalists by analyzing how reporters’ privilege cases, defamation law (“responsible journalism”) and ethical codes of conduct might affect and inform current Canadian case law. She compares how journalistic purpose exceptions are configured and applied in Australia and the United Kingdom. In the conclusion, the author considers the direction that data protection law in Canada should take. She suggests that a reasonableness test, which attempts to balance the various conflicting interests, should govern decisions on whether information is being provided for a journalistic purpose or for some “other” purpose.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it