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Record W3117320684 · doi:10.1787/7fbaed62-en

Data localisation trends and challenges

2020· paratext· en· W3117320684 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOECD digital economy papers · 2020
Typeparatext
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsPrivacy Analytics (Canada)
Fundersnot available
KeywordsData governanceRelevance (law)Context (archaeology)AccountabilityCorporate governanceProportionality (law)Information privacyComputer scienceData Protection Act 1998Work (physics)Data scienceData qualityComputer securityPolitical scienceInternet privacyBusinessEngineeringLawGeography

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.818
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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.

Opus teacher head0.080
GPT teacher head0.247
Teacher spread0.167 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it