Losing the Battle But Winning the War: Why Online Information Should Be a Prohibited Ground
Bibliographic record
Abstract
<p>This paper contends that in the “war” to protect the privacy of individuals’ personal information online, the battle to limit the collection of such information has been lost. Existing personal information protection regimes, with their emphasis on notice and consent, have proven inadequate, especially in light of the advent of “big data analytics” and revelations of large-scale privacy violations by governments and corporations. I argue, however, that the war can still be won on another front — that of limiting the use of personal information. In developing this theme, I explore the notion of “network privacy,” which posits that information shared online within a given social circle is intended to stay within that social circle, and is not to be shared beyond its boundaries without permission. Currently there is no legal protection in Canada against the invasion of network privacy (though in several recent decisions, the courts have shown a more nuanced understanding of privacy in online information). One potential source of such protection might be the adoption of the “Oxford principles” formulated in 2013, which propose a new model for regulating the processing of information, one that is focused on the use of personal information rather than on its collection. In my view, though, those principles, as well as other proposals, would not provide sufficient protection. Instead, I outline an approach that is broadly similar to the prohibition against the use of information relating to protected grounds under Canadian human rights legislation. Under this approach, no action could be taken against an individual — including in the employment context — based on his or her online information, except where that information reveals criminal, illegal or unethical conduct, or causes significant harm to others.</p>
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".