Digitalization of state financial control: ontological modeling as a tool for implementing a data-centric approach
Bibliographic record
Abstract
The move towards data-centric architectures is a global trend for both commercial and government structures. Therefore, it is undoubtedly interesting to consider issues related to improving state financial control within the paradigm of data-centric public administration and to develop a conceptual approach to collecting and analyzing information about controlled entities, taking into account the possibilities for expanding the set of information sources and types of data on companies’ activities. To date, there has been virtually no research in the Russian scientific community on the conceptualization of the subject area of financial control, and the existing work is fragmentary. Therefore, the aim of this work is to justify the feasibility of applying an ontological model in the field of digitalization of state financial control functions, as well as to develop a prototype ontological model of state (municipal) financial control. Based on an analysis of the experience of Russia and foreign countries (China, the United States, Canada, South Korea, Argentina, Brazil, India, etc.) in the field of digitalization of state financial control functions, organizational, managerial and technological recommendations for building an effective system of risk-oriented state (municipal) financial control have been formulated. The recommendations have been systematized and ranked using the GRAGE approach and expert assessment methods. A survey showed that experts are focusing on the development of standardized approaches to the collection and processing of heterogeneous data, as well as the development of an ontological model. The main focus is on building an ontology for the subject area of state financial control. It is proposed to use a combination of top-level ontology (the BFO basic formal ontology standardized in the Russian Federation) with a subject ontology developed taking into account the specifics of state financial control in the Russian Federation. The ontological engineering methodology was based on a fractal approach, the Methontology framework, and the methodology of visual-analytical thinking. The ontology specification was completed, a prototype meta-ontology was obtained, including the basic concepts of state financial control, and prototypes of category ontologies and data sources used in expert and analytical activities by state structures were constructed.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".