The Role of Agricultural Policies in Jordan to Mitigate the Effects of COVID-19 on the Agricultural Sector
Bibliographic record
Abstract
Since early 2020, an outbreak of the coronavirus disease 2019 (COVID-19) has started to spread in Jordan challenging the sustainability of Jordan’s economic sectors and the agricultural sector. A study was conducted in Jordan to evaluate the role of Jordanian Agricultural Policies to mitigate the effects of COVID-19 on the agricultural sector while its full impact on the Jordanian agricultural sector is not yet evident. A scientific questionnaire was distributed to 100 samples of Jordanians who had direct contact with the effect of agricultural policies and they can touch the effect of Coronavirus and Agricultural Policies on the agricultural sector, data selected from farmers, farm labour, fruits and vegetable traders and merchants, and Jordanian citizens were selected randomly from different areas in Jordan. The study summarizes target group opinion and some evidence on the different COVID-19 impacts on the Jordanian agricultural sector. The virus limits the free flow of labour, the agricultural labour force had a slightly decreased in 2020 compared with the year 2019 to about 7%, the country lockdown led to damage of crops due to lack of harvest and/or crop accumulation, as a result, the Jordan Agricultural Contribution to GDP growth rate at current prices was decreased 1.4%, and the growth rate at constant prices was also decreased 1.6%, the exported agricultural commodities value were not affected by COVID-19 pandemic but the imported of Agricultural commodities value was increased. Jordanian government try to facilitate the process of agricultural production and the provision of food during the Corona pandemic through issue agricultural policies and measures to alleviate the effects of the Corona pandemic on the agricultural sector.
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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 | 0.002 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".