Bibliographic record
Abstract
As we continue to advance through the digital century, our governments, crown corporations, municipalities, school boards, regional health authorities, health care providers, and others are enhancing the digital element of the services that they provide.Even for those services that continue to be provided in the traditional way, there is a greater level of data collection involved.Generally speaking, this is a good thing.Many services can be delivered more broadly and efficiently when done so digitally, and having more data about all services makes it easier to tailor and improve them.However, privacy regulators such as the author of this article -the Information and Privacy Commissioner for Newfoundland and Labrador -are watching closely and with concern as our governments and public bodies collect more and more information about us.Commissioners advocate for greater openness of public bodies, and this can mean greater disclosure of information, but also the need to advocate for strong privacy protection -which can extend from the privacy principle of minimizing the collection of data in the first place, through holding it securely and disclosing it only under strict conditions and for established purposes, to destroying it as quickly as possible when no longer needed.These principles are difficult to square with the needs of researchers, who naturally want to get access to as much data as they can, as quickly as they can.It is the view of the Office of the Information and Privacy Commissioner (OIPC) for Newfoundland and Labrador (NL) that data centres can provide a way to advance these principles.This article establishes what the interest of the Commissioner is in the matter, arising from my statutory mandate, and discusses how the OIPC is able to promote such data centres while maintaining sufficient arms length from actual data centres in order to preserve its independent regulatory oversight role.
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 | 0.249 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.013 | 0.043 |
| Scholarly communication | 0.054 | 0.049 |
| Open science | 0.014 | 0.016 |
| Research integrity | 0.074 | 0.041 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".