Bibliographic record
Abstract
Summary form only given. Agents are social and autonomous entities that can take great advantages of interaction modelling. Role-Based Collaboration (RBC)[4] is an emerging methodology to facilitate an organizational structure, provide orderly system behavior, and consolidate system security for both human and non-human entities that collaborate and coordinate their activities with or within systems. Interaction management must, however, be able to handle run-time and dynamic scenarios. Hence every RBC system must provide a good level of dynamism, that is providing the capability of an agent to assume, use and release a role depending on run-time conditions. In Object Oriented Programming languages, such as Java, role perceivability could be achieved with appropriate changes to the agent/entity class structure, but this requires compile-time constraints that are, in their nature, not dynamic. Moreover, other issues raise in the case when the agent is masked by a proxy or some other indirection level, since in this case it is difficult to perceive the played role because the agent is hidden. This poster proposes an approach (WhiteCat) to remedy the above problems: maintaining an appropriate level of dynamism. The work presented here allows a Java agent to make its role perceivable to other entities as if it is applied at compile-time. The presented approach was born in the agent scenario, but thanks to its modularity and flexibility it can be exploited and applied to other dynamic contexts.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".