Market expansion's influence on the harvesting of non-wood forest products in the Arasbaran forests of Iran
Bibliographic record
Abstract
Commercialization and expansion of the market for non-wood forest products (NWFPs) may increase the gathering and selling of these products, as well as their contribution to local livelihoods. The influence of market access on the type of dependency is significant. This study examines the relationship between market access and the harvesting of NWFPs as well as the limitations and problems in gathering and selling these products. Cornelian cherry, walnut and plum were the most commonly harvested species. Of the 13 NWFP species collected in Arasbaran forests, just three species, cornelian cherry, plum and pomegranate were sold in the local markets. The average contribution of NWFPs towards total household income was just 2.7%. The villages of cluster 1 had better access to markets and middlemen than did the villages in the other clusters and 63% of the local people in cluster 1 mentioned that they have increased their harvesting of NWFPs. Poor infrastructure, low or no access to markets, lack of market information, lack of cooperation, and low product prices were found to limit the potential economic benefits from harvesting NWFPs. In this situation, rural extension services can assist local people resolve many of these challenges and problems.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".