Internationaler Vergleich der Systeme zur Besteuerung der Land- und Forstwirtschaft
Bibliographic record
Abstract
Due to its overwhelming significance among taxes on earnings in the agricultural and forestry sector, the analyses are primarily concentrated on the international comparison of effective income tax burden in the selected EU Member States as well as Canada, the United States and Japan. In this context, it is particularly stressed that, instead of applying actual earnings, the determination of taxable profits (or income) for small-sized agricultural firms takes place on the basis of standard, blanket amount of earnings in several investigated countries. In addition, national rules on real property tax are introduced in a systematic way. Furthermore, on the basis of legal information existing in the individual countries, an attempt is made in the second step to quantify the impact of such different tax systems on the basis of a model calculation. Although the rules about public subsidies for agriculture and forestry in the EU appear to be largely harmonised, the varied tax treatment of such transfers as extra earnings leads, ceteris paribus, to different effective tax burdens in the individual countries. This fact, in turn, suggests urgent needs for further political discussions on the EU level. The study consists of two parts. The former provides a general, comparative overview of taxation in agricul-ture and forestry in the investigated countries. The latter, with the so-called country reports shows the relevant laws and rules in the individual countries in more detail.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".