Exporting Finnish fish products into German market
Bibliographic record
Abstract
The purpose of this thesis is to find out whether there is potential market for Finnish fish products in the grocery markets of Germany. \n The thesis is done to a Finnish company called Hätälä Oy. The company core business is to process fish products which it has mostly imported from Norway and Canada. They do use some Finnish fish also, but not to the extent that it could be exported. The thesis was conducted as a desktop study and as such it utilises secondary data. This is the case in most market studys. In the thesis the authors used databases such as Market Line, theories such as the PESTEL-analysis, field professional and consumer interviews to conduct a clear picture of the frozen fish segment of the German food market. \nResults suggest that consumer decision are guided mainly by two things: pricing being the overwhelming driver of purchase decisions and quality as a second motivator. Authors conclude that with the given information Hätälä does not have a competitive advantage in either one of the two major attributes driving purchasing decisions. Further studies in this field for Hätälä Oy has to be done with more intrest coming from the commissioners side. It is impossible to state with conviction that Hätälä products have no feasible chance in German markets as the authors did not get factual numbers from Hätälä Oy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.025 | 0.002 |
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".