Second Home Tourism and Agriculture in Rural Areas: Examining the Effects of Second Homes on Agricultural Resources in Northern Iran
Bibliographic record
Abstract
Second home tourism leads to new economic opportunities for local communities, and presents challenges for existing economic sectors in rural areas such as agriculture and l ivestock; it also fosters differing perceptions about the impact of appropriate development paths within rural areas. Despite the economic importance of second home tourism and its profound and often negative effects on the agriculture, no focused research of phenomenon has yet been conducted in Iran. The study aims to investigate second-home tourism in Iran with special reference to the perceptions of its positive and negative effects amongst second homeowners and local residents in the rural areas. The data were collected from 60 local household residents and 60 second homeowners randomly chosen. The data were first gathered through questionnaire, which was then used to analyse the second home tourism impacts on agriculture from the perception of tourism stakeholders. The research proves that uncontrolled second home development negatively affects natural attractions and agricultural resources in mountainous areas of Babol district, and overall northern Iran. Thus, there is more conflict existing between the two industries rather than synergy. Thereby, controlling second home tourism is one of the key factors for sustainable rural and agricultural development in the area.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".