E.valdžios taikymas valstybės sienos apsaugoje
Bibliographic record
Abstract
The subject of the work is “E-government usage in the defense of the state”, it aims to analyze electronic government politics in defense of state borders and describes the usage opportunities of the public electronic border area services in other countries with making suggestions for better usage in Lithuania. An author makes an analysis of e-border usage (which ensures border’s safety) in the foreign countries and Lithuania and also looks into its main directions of the development put into practice in other countries. The paper describes in detail the Schengens informational system (SIS) and analyses the basics of information entering into SIS. The EU e-border is described as a mean to fight illegal migration. The work shows the main program strategic priorities like collaboration with third countries, strengthen of the external borders, fight with human selling, departure politics, improving of the informational exchanges. Author analyses e-border’s installation peculiarities in The United Kingdom, Lithuanian e-border creation in the contexts of the EU and also shows the results of an analysis of the foreign countries (Canada, USA) and Lithuanian e-border services. In the end of the paper author make the conclusions.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".