Bibliographic record
Abstract
Terrorism seriously endangers world peace and security. In order to achieve the goal of effectively combating international terrorism, we must promote the establishment of a multi-level model of anti-terrorism cooperation. The current legal models of international counter-terrorism cooperation mainly include global counter-terrorism cooperation, regional counter-terrorism cooperation and bilateral counter-terrorism cooperation. Global counter-terrorism cooperation led by the United Nations is the most important form of counter-terrorism cooperation. The UN has set up a Counter-terrorism Committee and a series of treaties. The United Nations demands that States should prevent and stop the financing of terrorist ACTS; Criminalizes any person or thing that, by any means, directly or indirectly, provides or raises funds for terrorist activities; (b) Immediately freeze the assets of individuals and entities that facilitate, finance or participate in terrorist ACTS; The provision of any funds and financial assets and related services to individuals and entities assisting, financing or participating in terrorist ACTS is prohibited. Regional counter-terrorism cooperation refers to the cooperation between geographically adjacent countries to combat terrorism, which is an important part of international counter-terrorism cooperation. The main regional anti-terrorism cooperation organizations are: Association of Southeast Asian Nations, South Asian Association for Regional Cooperation, European Union, Organization of American States, Shanghai Cooperation Organization. Bilateral anti-terrorism cooperation refers to the cooperation between two parties in order to form a joint anti-terrorism force. Bilateral cooperation can be between states or between states and regional organizations. Bilateral cooperation in flexible and diverse forms is also an important form of counter-terrorism cooperation.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 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".