Identity Management Systems: Techno-Semantic Interoperability for Heterogeneous Federated Systems
Bibliographic record
Abstract
The identity management domain is a huge research domain. The federated systems proved on theirs legibility to solve a several digital identity issues. However, the problem of interoperability between federations is the researcher first issue. The researchers final goal is creating a federation of federations which is a large meta-system composed of several different federation systems. The previous researchers’ technical interoperability approach solved a part of the above-mentioned issue. However, there are some-others problems in the communication process between federated systems. In this work, the researcher target the semantic interoperability as a solution to solve the exchange of attribute issue among heterogeneous federated systems, because there is a significant need of managing the users’ attributes coming from different federations. Therefore, the researcher proposed a semantic layer to enhance the previous technical approach with the aim to guarantee the exchange of attribute that has the same semantic signification but a different representation, all that based on a mapping and matching between different anthologies. This approach will be applied to the academic domain as the researcher application domain.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".