THE ATTRACTION OF BOND FINANCING BY FOOD PRODUCING COMPANIES IN ARCTIC COUNTRIES
Bibliographic record
Abstract
The article contains an analysis of the established practices of attracting financing through the issuance of bonds on the example of companies in the agricultural and food sectors of the countries included in the Arctic zone: Denmark, Iceland, Canada, Norway, Russia, Finland, Sweden.The goal is to compile profiles of the bond market of individual countries and identify existing patterns.The research sample included 60 companies producing food products, which are also issuing companies in the debt market for the period 2015-2022.Conclusions are drawn, there are common institutional features in almost all the countries considered.Russia is characterized by an atypically large number of bonds placed, high market inertia, and high borrowing costs.On the Canadian market, the bond placement period is on average much shorter with a sufficiently large capital of organizations.In Canada, Denmark and Norway, there is a picture of hyper-concentration of the market with a focus on institutional investors.The situation is approximately similar with a much smaller number of bonds in Sweden, Iceland and Finland.The necessity of increasing the availability of the placed debt for private domestic investors was noted.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".