Prospects and challenges for developing countries in trade and production of organic food and fibers
Bibliographic record
Abstract
Especially after the Second World War, both in developed and to some extent developing countries, agriculture became highly mechanized and specialized as well as heavily dependent on agro‐chemicals. Such intensification of farming has produced higher yields and greater wealth but has also created some problems affecting the environment, food and farm‐worker safety. Today, different farming systems are subject to discussion and research to discover an alternative to this current agriculture such as organic production, to create conditions for sustainable agriculture and development. Organic farming has been developed in most Western countries such as the USA, Canada, Japan and European Union. In Turkey, organic farming is relatively new, but the number of producers involved in organic production activity has been increasing rapidly since the mid 1980s mainly by the relationships of some multinational companies in order to export organic products to the developed nations especially to the EU. Developing countries like Turkey have some advantages and also challenges for organic food and fiber production. First of all low‐input traditional systems are still a major part of those countries’ agriculture. Contrary to the developed countries, organic agriculture practices can help the farmers to increase productivity and to protect natural resources through using proper technological systems and also easy access to market and information. As in Turkey, most developing countries have small domestic organic markets and have begun to seize the export opportunities presented by organic farming. The main challenges they face are not having proper production practices, certification organizations and being tied only to foreign markets. It is a fact that development of organic production in these countries requires farmers to act in an organized manner instead of depending on a certification, export or import companies or a governmental body. The role of the government, of course, is also important to establish a legislative base and to support organic farming by convenient policy measures.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".