Bibliographic record
Abstract
World Wide Web and the graphic user agents(web browsers) have brought the internet to billions of new users who use it hours on end daily to perform a multitude of tasks. However, the user agents also provide a means to compromise the users privacy by employing various tracking mechanisms and use of analytics. The browser was intended to make the use of the internet easy with a simple and intuitive interface. It has morphed into a beast which has hidden in it mechanisms to allow suppliers of information content, on-line shopping companies and multitude of third parties to target publicity based on information gleaned from previous web journeys of users of these browsers. This paper focuses on summarizing privacy problems on the client side and highlights the default settings of some of the popular browsers and points out the difficulty of creating the proper settings even to disable cookies from third parties. We present some independent add-ons to help in preserving some privacy and some of the drawbacks of such band-aid solutions. Finally, we present some suggestions so that the user can know exactly what is being recorded in the cookies based on double encryption giving back some control to the user of his own data.
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 | 0.023 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".