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
As users in software systems depend on each other for achieving goals, performing tasks, and utilizing resources, the trust relationships in the systems need to be considered to identify the opportunities and vulnerabilities these relationships bring. However, the problem with specifying a trust relationship is that there is no precise and a priori criteria to be satisfied. The main objective of this work is towards incorporating trust from the very beginning of a software development process. A framework is presented for specifying trust scenarios using an extension of Unified Modeling Language (UML) called UMLtrust (UML for trust scenarios). A trust scenario combines interested parties based on a context and thus helps in building a trust relationship. Suitable trust rules can be generated from the trust scenarios to monitor the trustworthiness of specific trust relationships. In this way, we can avoid conflicting, ambiguous, and redundant trust requirements in a software development life cycle (SDLC). The applicability of the approach has been illustrated using examples from file sharing applications.
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 | 0.000 | 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