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Record W190662549

Internationaler Vergleich der Systeme zur Besteuerung der Land- und Forstwirtschaft

2001· preprint· de· W190662549 on OpenAlexaboutno aff
RÃ ⁄ diger Parsche, Peter Haug, Antonio Tomaz Marcelo, Chang Woon Nam, Bettina Reichl

Bibliographic record

VenueEconstor (Econstor) · 2001
Typepreprint
Languagede
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsTaxable incomeSubsidyEarningsContext (archaeology)Ceteris paribusEconomicsBusinessPublic economicsAccountingMarket economyGeography
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.008

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.

Opus teacher head0.023
GPT teacher head0.250
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2001
Admission routes1
Has abstractyes

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