Offshore Holdings Analytics Using Datalog+ RuleML Rules
Bibliographic record
Abstract
In April 2013, the International Consortium of Investigative Journalists (ICIJ) exposed the details of 130,000 offshore accounts. Although there are legitimate businesses which use such accounts, there exist a number of accounts which are possibly linked to international tax fraud and money laundering. The ICIJ investigation was based on 2.5 million records of offshore holdings linked to 170 countries. All these records have been made available for further examination and analysis. Based on these records, a set of facts, rules and queries have been formulated in Datalog+ RuleML 1.01/XML and these rules have been tested and validated against the Relax NG schema for Datalog+ in RuleML 1.01/XML. The usefulness of such rules for offshore holdings analytics is demonstrated here via incremental step-by-step approach, through the discovery of relationships between persons hiding large sums of assets and the offshore companies where the assets are purported to be hidden.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".