Exploratory Analysis of Transaction Data
Bibliographic record
Abstract
Transactions are fundamental components of an economy. This paper presents an analytic apparatus that can be used to analyze transaction data, where the transaction is the fundamental unit of observation. Transactions also are a potentially fundamental observation associated with the detection and characterization of organizational activities and events through acquisitions, trades or financial transactions. The objective of the research described in this paper was to develop a mathematical signature that represents transaction data (Point A to Point B, etc.), and visualize the transactions using currently available visualization tools. The representational signature should be useful for indicating change in organizational behavior, and for indicating when anomalous behavior occurs, i.e., something that is different than the common daily, quarterly or annual occurrence. The mathematical construct will be the same whether the transactions are country trade data or bank transactions, electrical grid transactions or some other multi-point transfer of information, asset, action, etc. The particular data example shown in this paper is international economic trade data for six countries; Mexico and its 5 largest trading partners, the United States, Germany, Canada, Japan and South Korea.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".