Bibliographic record
Abstract
Artificial intelligence (AI) drives demand for large quantities of data, including personal and human behavioural data. The links between personal data and many AI applications raise privacy concerns, as well as ethics and human rights issues. It is therefore unsurprising to see both the application of existing data protection laws in the AI context, and the amendment of those laws to address specific issues. Canada’s public and private sector data protection laws are outdated, and there have been numerous calls for their reform. Reforms must address both the need of organizations to access the large quantities of data needed for AI innovation as well as the imperative to properly protect the human right to privacy and data protection. In the European Union, the General Data Protection Regulation (GDPR) has strengthened privacy protections and has provided new rights tailored specifically to the contexts of big data, AI, and automated decision-making. The looming GDPR adequacy assessment for Canada, combined with the genuine need to modernize Canadian data protection laws, means that reform is coming—most likely federally and provincially, and for public and private sectors. The high demand for data and the need for ways to share data that respect both privacy and ethical considerations have also driven a growing interest in new data-sharing frameworks, some of which could be supported by legislative amendments.
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 | 0.055 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.042 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.022 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".