Bibliographic record
Abstract
It should first be noted that only ‘grey literature’ was identified for this helpdesk. Some \ninformation is included from government websites. Much of the material is commentary, included \nto give an idea of what is being said on this area. It must be taken into account that this \ninformation is conjecture. This rapid review found information on Canada, India, Iran, Israel, \nKenya, Serbia, South Africa, Trinidad and Tobago, the US, and the UK. \nAcross different country offices the key roles and responsibilities discussed in the literature for \nNational Security Adviser (NSA) offices include: Analysing security issues, assessing expected trends and prioritising activities; Playing an advisory role. Making recommendations to the Prime Minster or President; Policy making. In some countries the NSA make policies and in some countries the NSA review and make recommendations for policy-making; Coordinating and integrating work between different ministries. The degree of authority given to NSAs and National Security Councils varies between country and no one way has been identified as more or less successful. There are limited analyses of strengths and weaknesses of NSAs and NSCs in individual countries. There are also some analysis of how NSA responsibilities and functions in individual countries have changed over time. For example Best (2011) describes a history of the NSA in the US where different presidents used the NSA in different ways. \nOne of the problems identified with NSAs/NSCs is when responsibilities are poorly defined. It is important to identify who is responsible for what. Another problem identified across countries is lack of democratic or civilian control over NSAs/NSCs. A need for checks and balances is identified. The relationship of NSAs/NSCs to the military must also be clearly defined. Transparency of NSAs/NSCs is noted as important. As is the need for constitutional recognition of NSAs/NSCs role.
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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.128 | 0.082 |
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".