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
We present several extensions to the Nymble framework for anonymous blacklisting systems. First, we show how to distribute the Verinym Issuer as a threshold entity. This provides liveness against a threshold Byzantine adversary and protects against denial-of-service attacks. Second, we describe how to revoke a user for a period spanning multiple link ability windows. This gives service providers more flexibility in deciding how long to block individual users. We also point out how our solution enables efficient blacklist transferability among service providers. Third, we augment the Verinym Acquisition Protocol for Tor-aware systems (that utilize IP addresses as a unique identifier) to handle two additional cases: 1) the operator of a Tor exit node wishes to access services protected by the system, and 2) a user's access to the Verinym Issuer (and the Tor network) is blocked by a firewall. Finally, we revisit the objective blacklisting mechanism used in Jack, and generalize this idea to enable objective blacklisting in other Nymble-like systems. We illustrate the approach by showing how to implement it in Nymble and Nymbler.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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 it