Används ekonomiska planeringshjälpmedel i lantbruksföretag
Bibliographic record
Abstract
In agricultural educations you learn to use different economic planning tools, as basis for decision. The purpose and goal of this work has been to find out if economic planning tools are used by the farmers in Sweden. We choose to do an investigation through personal interviews, and used an inquiry to get as equivalent answers as possible from the farmers. This work contains a literature study with relevant facts about the questions that is brought up in the inquiry. This study of literature explains shortly some different calculation methods and bases for decision. It also contains useable information that is important to know before an investment. The questions that are asked to the farmers have focused on the use of calculation methods and economical planning tools. We wanted to get an overview of what farm firms use as basis for decisions. Therefor the study only contains a few and overall questions. In the investigation, eleven companies with varied econom ic turnover and orientations have As far as possible personal interviews has been done, we choose that method to get as truthful answers as possible. It has also been possible to explain the uestions more thoroughly, to avoid misunderstandings. Almost every company had more than one production orientation, but common for all companies where that they had some kind of crop cultivation. The main part of the companies had an economic turnover between three and ten million Swedish kronor. In the investigation, most of the companies used some kind of calculation method before an investment. This calculation was mainly performed by the farmers themselves, only a few companies hired external help. Only a few companies used liquidity calculations, and when it was used it was in connection with larger investments. One interesting result was that the main part of the companies did some kind of sensitivity analysis before an investment. One of the company manager pointed that this was one of the most important aspects in decision making before an investment. Cost accounting for production follow up was used by the main part of the companies, either in the whole company or in special production branches. All the farmers answered every question in the inquiry except for the last two questions that was not necessary because of the answers in the earlier questions. The uses of economic planning tools are widely extent in farm firms, and that shows that it is important for decision making in the firms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.015 |
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".