Más medidas de ciberseguridad internacional: Armenia, Australia, Bosnia y Herzegovina, Canadá, Dinamarca, Emiratos Árabes Unidos, Georgia, Honduras e Indonesia (More International Cybersecurity Measures: Armenia, Australia, Bosnia and Herzegovina, Canada, Denmark, Georgia, Honduras, Indonesia, and the United Arab Emirates)
Bibliographic record
Abstract
Spanish Abstract: La hiperconectividad de la poblacion y la busqueda de una digitalizacion integral de las organizaciones han permitido un nuevo camino para la comision de delitos, que, con una gran asimetria entre los recursos invertidos por los ciberdelincuentes y el dano economico que pueden producir, conciernen a todos los gobernantes del mundo. En este articulo se evaluan las principales medidas de seguridad cibernetica que los Estados de Armenia, Australia, Bosnia y Herzegovina, el Canada, Dinamarca, los Emiratos Arabes Unidos, Georgia, Honduras e Indonesia han comunicado al Secretario General de las Naciones Unidas, y que este ha incluido en uno de sus ultimos informes sobre el tema. English Abstract: The hyperconnectivity of the population and the search for a comprehensive digitization of organizations have allowed a new path for the commission of crimes, which, with a great asymmetry between the resources invested by cybercriminals and the economic damage they can produce, concern all rulers of the world. This article assesses the main cybersecurity measures that the States of Armenia, Australia, Bosnia and Herzegovina, Canada, Denmark, United Arab Emirates, Georgia, Honduras and Indonesia have communicated to the Secretary General of the United Nations, and which he has included in one of his latest reports on the subject.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".