Sunlight alone is not a disinfectant: Consent and the futility of opening Big Data black boxes (without assistance)
Bibliographic record
Abstract
In our attempts to achieve privacy and reputation deliverables, advocating for service providers and other data managers to open Big Data black boxes and be more transparent about consent processes, algorithmic details, and data practice is easy. Moving from this call to meaningful forms of transparency, where the Big Data details are available, useful, and manageable is more difficult. Most challenging is moving from that difficult task of meaningful transparency to the seemingly impossible scenario of achieving, consistently and ubiquitously, meaningful forms of consent, where individuals are aware of data practices and implications, understand these realities, and agree to them as well. This commentary unpacks these concerns in the online consent context. It emphasizes that self-governance fallacy pervades current approaches to achieving digital forms of privacy, exemplified by the assertion that transparency and information access alone are enough to help individuals achieve privacy and reputation protections.
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 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 | 0.088 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.071 |
| Scholarly communication | 0.015 | 0.029 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.020 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".