Az állampolgárok állatvédelemmel kapcsolatos jogszabályi ismereteinek kérdőíves felmérése
Bibliographic record
Abstract
During my work at The Veterinary Administration and Agricultural Economics \nDepartment, I examined how much familiar Hungarian people are with the legislation \nregarding protection of animals, and whether the farmers are aware of their legal obligations, \nor not. The research was conducted through a questionnaire survey, anonymously. Responses \nof 441 completed questionnaires were processed and evaluated according to the rules of \nmarket research. 61.1% of the fillers were 20-40 years old, slightly more than a quarter \n(25.5%) were 40-60 years old, while 8.6% were underthe age of 20 and 4.8% belonged to the \nage group above 60 years. Specific activities related to animal welfare were admitted in \n24.3% of cases. Our survey revealed that farmers are very often false informed regarding the \nanimal protection laws and the percent of them giving a bad answer ranged between 25 and \n75%, but there was a question to which 80% of them gave the wrong answer.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.242 | 0.002 |
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; both teacher heads agree on what is shown here.
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".