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.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".