Bibliographic record
Abstract
In today's age of exposure, websites and Internet services are collecting personal data-with or without the knowledge or consent of users. Not only does new technology provide an abundance of methods for organizations to gather and store information, but people are also willingly sharing data with increasing frequency, exposing their intimate lives on social media websites such as Facebook, Twitter, You tube, My space and others. Moreover, online data brokers, search engines, data aggregators and many other actors of the web are profiling people for various purposes such as the improvement of marketing through better statistics and an ability to predict consumer behaviour. Other less known reasons include understanding the newest trends in education, gathering people's medical history or observing tendencies in political opinions. People who care about privacy use the Privacy Enhancing Technologies (PETs) to protect their data, even though clearly not sufficiently. Indeed, as soon as information is recorded in a database, it becomes permanently available for analysis. Consequently even the most privacy aware users are not safe from the threat of re-identification. On the other hand, there are many people who are willing to share their personal information, even when fully conscious of the consequences. A claim from the advocates of open access information is that the preservation of privacy should not be an issue, as people seem to be confortable in a world where their tastes, lifestyle or personality are digitized and publicly available. This paper deals with Internet data collection and voluntary information disclosure, with an emphasis on the problems and challenges facing privacy nowadays.
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 | 0.000 | 0.006 |
| 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 | 0.039 | 0.115 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".