Bibliographic record
Abstract
This report highlights a complex situation in which some forms of data localisation are seen as useful and largely uncontroversial, while others as a significant barrier to the digital economy. Contributing to the review of the implementation of the OECD Privacy Guidelines, the report emphasises the need to recognise the effect that data localisation can have on transborder data flows, but suggests that the conditions that data privacy laws traditionally impose do not necessarily amount to data localisation measures. Focusing on data localisation in the context of data privacy and the governance of globalised data flows, the report proposes a definition for data localisation, outlines a roadmap to ensure that data localisation does not impede transborder data flows, and makes recommendations to support such work. In particular, it emphasises the relevance of the accountability principle and the proportionality test articulated in the OECD Privacy Guidelines in evaluating data localisation measures.
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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 | 0.022 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.017 | 0.043 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.015 |
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".