Bibliographic record
Abstract
Alice is reading a novel on her computer in the comfort of her home drinking a piña colada. Her husband John, on the other hand, is grabbing his morning cup of coffee at his favorite café while looking at his Facebook account. Alice’s webcam is on and John feels at ease using the café’s public WiFi. Then, suddenly, Alice gets an advertisement about Caribbean drinks. She is a bit perplexed. As for John, he receives an email from his boss asking why he is not at work and another from Facebook alerting him of suspicious activities on his account. He is irritated. They both thought their privacy was safe. There are a multitude of threats looming on the horizon from profiling, identity theft, and mass surveillance to depriving babies of their right to privacy before being born. Before diving into the dangers and how to eliminate or at least mitigate threats, let us understand the concept of privacy. To do so, we go back to the origins in order to comprehend its evolution throughout history. At each period, privacy practices adapt to the time-specific context. Not only that, but the historical context can have long-lasting implications for centuries to come. We then explain the situation today with the impacts on the individual and society. Finally, we draw some conclusions about the future of privacy.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.015 | 0.030 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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.
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".