Bibliographic record
Abstract
It is here. May 25 of this year marked the effective date of the most comprehensive privacy regulations on the planet. But most organisations in the US and Canada are probably not compliant with the European Union's sweeping new General Data Protection Regulation (GDPR) that has now taken effect. The General Data Protection Regulation (GDPR) is here and these tough new rules affect any company, government agency or other organisation that does business in Europe or handles the personal data of EU citizens or residents. There's no magic wand an organisation can wave to suddenly become compliant. And there's a surprising lack of awareness or concern about these regulations in the US. Gary Miglicco of PCM explains how GDPR means a dramatic change in how US companies manage their customers’ private data, and that it is only the start of a worldwide swing towards greater protection of consumers’ information.
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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.088 | 0.068 |
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".