Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Directional data is an important class of data where the magnitudes of the data points are negligible. It naturally occurs in many real-world scenarios: For instance, geographic locations (approximately) lie on a sphere, and periodic data such as time of day, or day of week can be interpreted as points on a circle. Massive amounts of directional data are collected by location-based service platforms such as Google Maps or Foursquare, who depend on mobility data from users' smartphones or wearable devices to enable their analytics and marketing businesses. However, such data is often highly privacy-sensitive and hence demands measures to protect the privacy of the individuals whose data is collected and processed. Starting with the von Mises-Fisher distribution, we therefore propose and analyze two novel privacy mechanisms for directional data by combining directional statistics with differential privacy, which presents the current state-of-the-art for quantifying and limiting information disclosure about individuals. As we will see, our specialized privacy mechanisms achieve a better privacy-utility trade-off than ex post adaptions of established mechanisms to directional 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.
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 it