Bibliographic record
Abstract
The crisis triggered by the novel COVID-19 coronavirus has rekindled the debate about the protection of personal information at least twice: through population tracking by means of cellular-based data and by way of data disclosure via data sharing within the healthcare network. Such a situation enables us to realistically test – i.e. prove – the reliability of some of our information governance instruments or mechanisms and their level of integration. As a matter of fact, a deficiency of integration between those informational instruments and a lack of consistency can give rise to circumstances where their respective objectives may become conflicting, and which may cause bad decisions (or type 1 errors). This is illustratively the case when a first law is enacted to promote the public good while a second law also designed to foster the public well-being is, to be effective, contingent on the implementation of measures that contravene or breach the first. For instance, let us consider the Privacy Act of Canada (R.S.C., 1985, c. P-21) and the Public Health Act of Quebec (CQLR c. S-2.2). While both of these laws are for the fostering of public good, certain provisions of the Public Health Act of Quebec (CQLR c. S-2.2), to be effective, require that they infringe or encroach on the objectives of the Privacy Act of Canada (R.S.C., 1985, c. P-21). What can be the consequences of such incompatible or contradictory situations?
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".