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.
Bibliographic record
Abstract
This paper analyses the challenges of blockchain technology in terms of General Data Protection Regulation compliance. It focuses on the great promise of transparency in blockchain systems, its tamper-proof nature and the question of ‘Who will be the data controller in blockchain transactions?’ In blockchain systems, data is distributed across all the nodes’ (who created the blocks) computers at different locations. This makes it difficult to amend the data in the blocks as all the nodes’ participation and approval are needed. While this feature provides transparency and resilience, it is a major challenge on exercising the rights of rectification or deletion. Blockchain systems can be created as private and public, and particularly in public blockchain systems, it can be challenging to allocate and determine the liability of the parties and determine the data controller. We look into the challenges in these areas in a nutshell and very briefly mention how blockchain can benefit from the privacy by design concept.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it