Animals: ‘objects’ or ‘sentient beings’? A comparative perspective of the South African law
Bibliographic record
Abstract
This paper provides a comparison of the law related to the classification of animals as either "legal objects" or "sentient beings" and "non-human persons."In this paper, the definition of "objects" in the South African Law of Persons will be explored.An explanation of the difference between a legal "object" and a legal "subject" in South African law will be provided.Legal research is done with the focus being Interpretative Research.In order to understand the classification of animals in South African law, the relevant provisions of the Animal Protection Act 71 of 1962 and the Performing Animals Protection Act 24 of 1935 (as amended) will be explained.Secondly, the classification of animals in the law of other countries will be explored.Examples will include France, where the legal status of animals has changed from that of "personal property" to "sentient beings"; New Zealand, where the Animal Welfare Amendment Act 2 of 2015 recognizes animals as sentient beings; the legal reforms in Quebec, Canada, stating that animals are not objects and the declaration by India that Cetaceans are "non-human persons."Implications of this research for practice may include a reclassification of animals as persons, which would result in the need for changes to be made in South African law.In conclusion, suggestions are made about whether the classification of animals as "objects" in South African law should be revised.
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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".