Data Driven Elections and Political Parties in Canada: Privacy Implications, Privacy Policies and Privacy Obligations
Bibliographic record
Abstract
In light of the revelations concerning Cambridge Analytica, we are now in an era of heightened publicity and concern about the role of voter analytics in elections. Parties in Canada need to enhance their privacy management practices and commit to complying with national privacy principles in all their operations. As shown in this article’s comparative analysis of the privacy policies of federal and provincial political parties in Canada, policies are often difficult to find, unclear, and, with a couple of exceptions, do not address all the privacy principles. Accountability and complaints mechanisms are often not clearly publicized, and many are silent on procedures for the access and correction of data, and unsubscribing from lists. Vague and expansive statements of purpose are also quite common. However, this article shows that parties could comply with all 10 principles within the Canadian Standard Association (CSA)’s National Standard of Canada, upon which Canadian privacy law is based, without difficulty; though compliance will require a thorough process of self-assessment and a commitment across the political spectrum to greater transparency. The early experience in British Columbia (B.C.), where parties are regulated under the provincial Personal Information Protection Act, suggests that this process is beneficial for all concerned. In contrast to the system of self-regulation incorporated into the Elections Modernization Act, there is no inherent reason why parties could not be legally mandated to comply with all 10 principles, under the oversight of the Office of the Privacy Commissioner of Canada.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".