MétaCan
Menu
Back to cohort
Record W4411570511 · doi:10.4324/9781032641089-15

Intelligence and security

2025· book-chapter· en· W4411570511 on OpenAlexaboutno aff
Andrew E. Moran

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicIntelligence, Security, War Strategy
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceComputer security

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.492
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.036
GPT teacher head0.317
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicIntelligence, Security, War StrategyFrench-language works237,207