Proceedings of the eighth ACM symposium on Access control models and technologies
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 8th ACM Symposium on Access Control Models and Technologies - SACMAT 2003. SACMAT continues with the tradition, first established by the ACM Workshop on Role-Based Access Control, of being the premier forum for presentation of research results and experience reports on leading edge issues of access control, including models, systems, applications, and theory. The mission of the symposium is to share novel access control solutions and identify new directions for future research and development work. SACMAT gives researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of access control.A total of 63 papers were submitted this year from Asia, Canada, Europe, and the United States. The papers in these proceedings were evaluated for their technical contribution, originality and impact to the field of access control and authorization management. By limiting the program to a single panel, and extending the conference a half day, the program committee was able to increase the number of accepted papers from 17 to 23. These papers cover a variety of topics to include, access control and administrative models, access control systems and applications, policy context and specification, and range from the highly theoretical to real world commercial implementations.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.053 | 0.013 |
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".