Bibliographic record
Abstract
This chapter will explore how the collapse of the Soviet Union has seen the role of the intelligence agencies redirected towards a growing number of security issues, including international crime, weapons proliferation, the problems of cyberspace, and the spread of terrorism. This movement away from state-centric surveillance has led to an explosion in intelligence gathering. Furthermore, the spread of globalisation has also undermined the traditional Westphalian state-centric model, which has led to an increasing integration of foreign intelligence operations and domestic surveillance, and international cooperation between intelligence agencies. An update to this chapter, for example, will include a discussion of the Five Eyes, an intelligence alliance involving the US, UK, Australia, New Zealand and Canada. The role of intelligence agencies remains controversial, however, particularly in democratic countries where openness and accountability are important. Indeed, revelations such as those by Edward Snowden have ignited a debate about privacy and transparency, whilst the leaking of documents implicating the US (and the UK) in extraordinary rendition, torture, and secret detention sites has raised fundamental questions surrounding some activities carried out by the intelligence agencies in the name of protecting the security of citizens. These will be considered, along with the trade-offs that might have to be made.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.017 |
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".