Bibliographic record
Abstract
within the framework of the North American Free Trade Agreement (NAFTA). The development of the world economy leads to the growing influence of economic integration, including integration in the insurance sector. Globalization makes it necessary for countries to work together to improve the stability of national financial systems. Along with the positive effects of integration, such as higher trade volumes, there are also negative repercussions, such as local producers being driven out of the market. The effects and risks associated with the influence of integration groups, identified in the course of research and discussions in economics, can be verified (or falsified) empirically on the basis of data on the development of national insurance markets within the framework of integration groups. Based on statistical data, econometric models were built to determine the effect of NAFTA on the Canadian insurance market by assessing the extent to which the changes in quantitative indicators of the development of the insurance market in Canada, namely the premium volume, insurance density and penetration, are due to the agreement. Based on existing insurance market research, a number of economic and social factors were selected as macroeconomic parameters affecting the premium volume. The author concludes that being a member of NAFTA does not affect the selected quantitative insurance development indicators in Canada. The author assumes this to be one of the reasons
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".