Bibliographic record
Abstract
The Journal of Intelligence, Conflict, and Warfare is pleased to publish the following thought piece from one of our esteemed Speakers from the 2020 West Coast Security Conference. The author, Mr. Dalip, is a lawyer working in the financial crime and corruption sphere. From 2015 to 2018, Mr. Dalip was a chairman at the Steering Group Planning Committee for the Caribbean Financial Action Task Force (CFATF); and from 2014 to 2018, he was a special legal advisor to the Ministry of Attorney General Trinidad and Tobago. The intersection between corruption and intelligence is gaining increased focus. Foreign intelligence services have an anti-corruption role at the strategic level through Intelligence Risk Assessments and at the operational level during post-conflict operations. Intelligence assessments of the effectiveness of non-kinetic tools on target countries also guide implementation and policy changes. The roles of security intelligence and foreign intelligence services are, however, no longer always discrete, particularly in the context of non-state actors. Foreign intelligence services would benefit from the skill sets of security intelligence agencies in detecting corruption related predicate offences, both in performing their core roles and supporting law enforcement operations. This includes the use of financial intelligence as well as other key open source intelligence resulting from anti-money laundering frameworks, the development of which has been driven globally by the Financial Action Task Force. In performing these roles, intelligence agencies must also be mindful of their own vulnerability to corruption.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".