Bibliographic record
Abstract
Recently, a stringent set of privacy regulations, the General Data Protection Regulation (GDPR), was enacted in the European Union, which can be considered a privacy non-functional requirement (NFR). As a result, an organization that collects or processes data from European citizens must adhere to the GDPR. Previous studies have shown that compliance to the GDPR poses a number of challenges, which we have confirmed in our own research. In this paper, we describe our ongoing research collaboration with a startup organization that is adopting the GDPR. In addition, during the course of our research, we found that our industry collaborator, practices continuous integration (CI) like many other organizations. The number of organizations adopting CI has increased since Fowler first published his definition of CI. As such, another aspect of our current research is exploring the effects of CI on privacy NFRs and other NFRs. Finally, we describe a design science approach to iteratively learn about industry challenges in GDPR compliance, NFRs in the context of CI, as well as our ongoing work creating a tool to potentially mitigate observed GDPR compliance challenges.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".