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Record W2254997580 · doi:10.4324/9780203849897-11

The eroding distinction between intelligence and evidence in terrorism investigations

2010· article· en· W2254997580 on OpenAlexaffabout
Kent Roach

Bibliographic record

VenueSSRN Electronic Journal · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCircumstantial evidenceAdversarial systemHuman intelligenceTerrorismIdeal (ethics)Government (linguistics)LawNational securityPolitical sciencePsychologySociologyCriminologyPhilosophy

Abstract

fetched live from OpenAlex

These lines from Graham Greene’s classic Cold War novel, The Human Factor, capture traditional differences between intelligence and evidence. Intelligence is ‘patchy’2 and ‘circumstantial’ information about perhaps remote risks to national security. It can be contrasted with hard evidence that can withstand adversarial challenge in court. The proponent of the intelligence paradigm in The Human Factor is Dr Percival. He has no time for legal niceties or public trials. He poisons poor Arthur Davis, who turns out to be the wrong man, innocent of the charge of being a Russian mole in the British Secret Service. The proponent of an evidence-based approach is Colonel Daintry, nominally in charge of security but who is handicapped by having read law at Oxford University. Much water has crossed under the bridge since Greene wrote these words in the 1970s. Nevertheless, they provide an appropriate starting point for a discussion of the distinction between intelligence and evidence. The ideal types of intelligence and evidence are rooted in a Cold War consensus.Intelligence could be collected to inform government about security risks with the expectation that it would never be publicly disclosed beyond the narrow range of those who ‘need to know’ (and alas the occasional mole). In contrast, evidence was collected after a crime had been committed. It could be subject to cross-examination and adversarial challenge and would be used in a public trial to prove guilt beyond a reasonable doubt. These ideal types highlight the preventive aspirations of intelligence and the truth-seeking and retributive ideals of evidence. Although there have always been departures from the ideal types, the creation ofsweeping new terrorism offences after the 9/11 attacks in the United States has blurred the traditional distinctions between intelligence and evidence. These new offencesreflect an intelligence mindset that focuses on threats, risk, associations and suspicion as opposed to an evidence or criminal law mindset that focuses on acts, accomplices and guilt. One implication of the blurring of the distinction between intelligence and evidence is a convergence between the work of police forces and security intelligence agencies in terrorism investigations. This convergence is driven in part by the demands of prevention. The imperative that the dots must be connected before another major terrorist attack occurs is forcing police forces and intelligence agencies to work together more closely both at home and abroad. It is also likely to result in a greater tolerance for false positives, in which the innocent are identified as security risks or even arrested and charged with new offences designed to aid in the prevention of terrorism. The convergence is also being driven by a practical recognition that intelligence may have evidential value especially with respect to many new broad terrorism offences that criminalise remote acts of preparation and various forms of association. Intelligence may sometimes also be subject to disclosure to the accused in terrorism trials. The convergence between intelligence and evidence, and between the role ofsecurity intelligence agencies and the police, is not without difficulties. Sharp growing pains have been felt in both Australia and Canada. Police forces in the Dr Mohamed Haneef affair in Australia and in the Maher Arar affair in Canada made well-publicised mistakes in interpreting and using intelligence. In both cases, national police forces acted on patchy and inaccurate intelligence. Individuals who only had innocent associations with terrorist suspects were harmed in both cases. In the case of Arar, who was detained for almost a year and tortured when rendered from the United States to Syria, the harms were extreme. There are reasons to believe that domestic security intelligence agencies might not have made the same mistakes as the police did in these cases. The Australian Security Intelligence Organization (ASIO) did not view Haneef as a security threat and Arar was not included in the list of suspects that the Canadian Security Intelligence Service (CSIS) handed over to the Royal Canadian Mounted Police (RCMP) in the wake of 9/11. Intelligence agencies have more experience and expertise than the police in dealing with fragmentary intelligence and they may be less tempted to conclude that patchy intelligence is evidence of guilt. If the police have proved to be uncomfortable in the new world of intelligence,intelligence agencies have been just as uncomfortable in dealing with evidence. The failures of intelligence agencies in this regard can be seen in ASIO’s missteps in interviewing Izhar Ul-Haque without proper attention to his rights and in CSIS’s missteps in the Air India investigation, a number of security certificate cases and in its interrogation of a teenaged Omar Khadr at Guantanamo Bay. In all these cases, intelligence agencies have failed to respect basic legal standards with respect to the collection, retention and disclosure of evidence. The results of such missteps have been the dismissal of the prosecution in the Ul-Haque case and, in Canada, a series of adverse judicial rulings that CSIS has violated legal rights.3

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.336
Teacher spread0.292 · 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
GenreEmpirical

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

Citations28
Published2010
Admission routes2
Has abstractyes

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