Bibliographic record
Abstract
Scalable trusted computing seeks to apply and extend the fundamental technologies of trusted computing to large-scale systems. To provide the functionality demanded by users, bootstrapping a trusted platform is but the first of many steps in a complex, evolving mesh of components. The bigger picture involves building up many additional layers to allow computing and communication across large-scale systems, while delivering a system retaining some hint of the original trust goal. Not to be lost in the shuffle is the most important element: the system's human users. Unlike 40 years ago, they cannot all be assumed to be computer experts, under the employ of government agencies which provide rigorous and regular training, always on tightly controlled hardware and software platforms. It seems obvious that the design of scalable trusted computing systems necessarily must involve, as an immutable design constraint, realistic expectations of the actions and capabilities of normal human users. Experience shows otherwise. The security community does not have a strong track record of learning from user studies, nor of acknowledging that it is generally impossible to predict the actions of ordinary users other than by observing (e.g., through user experience studies) the actions such users actually take in the precise target conditions. We assert that because the design of scalable trusted computing systems spans the full spectrum from hardware to software to human users, experts in all these areas are essential to the end-goal of scalable trusted computing.
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.
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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".