Bibliographic record
Abstract
Canadians increasingly rely on digital technology to connect with each other, to work and innovate. That’s why the Government of Canada is committed to making sure Canadians can benefit from the latest technologies, knowing that their privacy is safe and secure, and that companies are acting responsibly. In June 2022, the government proposed the Digital Charter Implementation Act, 2022, which will significantly strengthen Canada’s private sector privacy law, create new rules for the responsible development and use of artificial intelligence (AI), and continue advancing the implementation of Canada’s Digital Charter. Canada's Digital Charter sets out principles to ensure that privacy is protected, data-driven innovation is human-centred, and Canadian organizations can lead the world in innovations that fully embrace the benefits of the digital economy. Canadians must be able to trust that their personal information is protected, that their data will not be misused, and that organizations operating in this space communicate in a simple and straightforward manner with their users. This trust is the foundation on which Canadian digital and data-driven economy will be built. This legislation takes a number of important steps to ensure that Canadians have confidence that their privacy is respected and that AI is used responsibly, while unlocking innovation that promotes a strong economy. The Digital Charter Implementation Act, 2022 will include three proposed acts: the Consumer Privacy Protection Act, the Personal Information and Data Protection Tribunal Act, and the Artificial Intelligence and Data Act.
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 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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.021 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.073 | 0.012 |
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".