INTERNATIONAL EXPERIENCE OF STATE REGULATION OF THE FOOD INDUSTRY IN THE CONTEXT OF DIGITAL TRANSFORMATION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The article is devoted to researching the international experience of state regulation of the food industry in the conditions of digital transformation and determining ways of its adaptation to Ukrainian realities. The experience of the EU, the USA, China, Canada, Australia and India in the application of digital transformation technologies and innovations in the state regulation of food business was studied. The key factors for the successful implementation of digital technologies in the regulatory mechanisms of the food industry have been identified, including: the creation of a legal framework, investment in the development of digital infrastructure, increasing the digital literacy of employees and consumers, as well as cooperation between state bodies. The results of the conducted research emphasize the need for an integrated approach combining technological innovation, cooperation between governments, industry and scientific institutions, as well as strict quality standards. The integration of advanced technologies and partnerships between the government and the private sector are key elements for the sustainable development of the food industry.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it