MétaCan
Menu
Back to cohort
Record W1914365750 · doi:10.4102/jef.v5i2.299

‘n Ondersoek na die inkomstebelastinghantering van beëindigingsboetes betaalbaar deur verhuurders by die voortydige beëindiging van ‘n huurooreenkoms

2012· article· en· W1914365750 on OpenAlexaboutno aff
Leonard Willemse, David Frederick Badenhorst

Bibliographic record

VenueJournal of Economic and Financial Sciences · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Issues in South Africa
Canadian institutionsnot available
Fundersnot available
KeywordsLeaseEconomicsContext (archaeology)Profit (economics)Database transactionIncome taxBusinessFinancePublic economicsMicroeconomics

Abstract

fetched live from OpenAlex

The premature termination of lease agreements is a common occurrence in the South African and international business arena. When a lease is terminated prematurely, it is currently the practice that the person who terminates the lease agreement has to pay a termination penalty. This article investigates the income tax treatment possibilities of the penalty paid by a lessor. For purposes of this investigation the income tax treatment of lease termination penalties in Australia, Canada, the United States of America and South Africa are investigated. This is done in order to identify guidelines and principles that could possibly be used in a South African context, which may lead to the efficient and correct treatment of lease termination penalties for South African income tax purposes. The investigation concludes that the factors surrounding the lease termination transaction as well as the intention of the parties involved, will determine the appropriate income tax treatment of the penalty. The question must be asked whether or not the termination penalty was incurred as part of a ‘profit-making scheme’ and what happens after the penalty has been incurred. It is recommended that, where the penalty is deemed to be capital in nature, the merit of allowing some sort of capital allowance (similar to the one used in the United States of America) should be investigated.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.283
Teacher spread0.260 · 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; a candidate call from one teacher head, not a consensus.

Study designNot applicable
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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Economic and Financial SciencesSame topicLegal Issues in South AfricaFrench-language works237,207