Data protection in the UK post Brexit: The only certainty is uncertainty
Bibliographic record
Abstract
The EU General Data Protection Regulation (GDPR) was published in the Official Journal of the European Union on May 4 2016 . The GDPR replaces the 1995 Data Protection Directive (Directive 95/46/EC). After a two-year transition period, the GDPR will be binding on all member states including the UK, from 25 May 2018. \nSubsequent to the referendum result on June 23, 2016 to leave the European Union, the UK invoked Article 50.2 of the Treaty on European Union (TEU) and notified the EU of intention to withdraw from the EU membership on 29 March 2017. Pursuant to Article 50.3 TEU, following this notification the UK has two years to negotiate a new trading relationship with the EU \nSet against this background, this paper will critically examine the implications of Britain’s exit from the EU (hereinafter Brexit) on data protection law in the UK with a particular focus on the various trade models available to the UK post Brexit. There are various trade models available to the UK in terms of exiting the EU, such as the EEA model, the Swiss model, the free trade agreement as adopted by Canada, as well as the WTO model. Regardless of the agreed trading model, the GDPR will continue to be relevant for many organisations and businesses in the UK, as long as they wish to operate within the EU and transfer data across borders.\nThis article contends that irrespective of the model chosen for exiting the European Union, the UK will adopt standards almost identical to the GDPR in order to remain a competitive actor in the global economy. Nevertheless, even if the UK endeavours to adopt the same as or equivalent standards to the GDPR as a third country, this does not necessarily secure an adequacy decision from the EU commission, potentially leading to burdensome requirements for UK businesses and their trading partners.
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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 | 0.025 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.028 | 0.016 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.022 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".