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
Although location-based applications have existed for several years, verifying the correctness of a user’s claimed location is a challenge that has only recently gained attention in the research community. Existing architectures for the generation and verification of such location proofs have limited flexibility. For example, they do not support the proactive gathering of location proofs, where, at the time of acquiring a location proof, a user does not yet know for which application or service she will use this proof. Supporting proactive location proofs is challenging because these proofs might enable proof issuers to track a user or they might violate a user’s location privacy by revealing more information about a user’s location than strictly necessary to an application. We present six essential design goals that a flexible location proof architecture should meet. Furthermore, we introduce a location proof architecture that realizes our design goals and that includes user anonymity and location privacy as key design components, as opposed to previous proposals. Finally, we demonstrate how some of the design goals can be achieved by adopting proper cryptographic techniques. 1.
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 | 0.001 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.030 | 0.068 |
| Research integrity | 0.000 | 0.001 |
| 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 it