Agritourism activity as an example of diversification of agriculture.
Bibliographic record
Abstract
A characteristic feature of human behavior is their poly motivations, which means that the person taking deliberate action is guided by more than one motive. Deciding on the economic activity we are also choosing a number of motives. The paper presents the analysis of farm agritourism activity with particular zoom on the motivations and profitability of the surveyed farms. The study was undertaken to analyze the function of tourism farms with particular emphasis on the motivations and profitability of the surveyed households. Due to the fact that the motives for taking up business in tourism are similar in many countries, a study by the author allowed the separation of the three main motives for taking up agritourist farms surveyed in Western Pomerania Region. An important objective of the study was the relationship between the motives and activities undertaken at the farm source of income, age and education of respondents. Also determined the degree of economic viability as a result of agritourism ctivity undertaken, opportunities and barriers to their development. Data were collected from the original character of the agricultural operators in the rural areas of the West Pomeranian region which was conducted in the first quarter of 2011 and one hundred randomly farms were selected. Method of measurement was the questionnaire method and a questionnaire as a research tool.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".