Bibliographic record
Abstract
THIS PAPER PROPOSES A CLASSIFICATION for techniques that encourage, preserve, or enhance privacy in online environments. This classification encompasses both automated mechanisms (those that exclusively or primarily use computers and software to implement privacy techniques) and nonautomated mechanisms (those that exclusively or primarily use human means to implement privacy techniques). We give examples of various techniques and show where they fit within this classification. The importance of such a classification is discussed along with its use as a tool for the comparison and evaluation of privacy techniques. CET ARTICLE PROPOSE UNE CLASSIFICATION des techniques qui cherchent a encourager, a preserver et a ameliorer la protection de la vie privee dans les environnements en ligne. Cette classification comprend des mecanismes a la fois automatises (dont la mise en œuvre se fait exclusivement ou principalement a l’aide d’ordinateurs et de logiciels) et non automatises (dont mise en œuvre se fait exclusivement ou principalement par l’intermediaire de personnes). Des exemples sont donnes de diverses techniques, en les situant dans cette classification. L’article commente l’importance des classifications de ce genre ainsi que leur utilite pour la comparaison et l’evaluation des techniques pour la protection de la vie privee. 35
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".