Comparison of The Change in Total Agricultural Productivity Between the Groups of Developing and Developed Countries Using the Malmquist Method for The Period (1990-2017)
Bibliographic record
Abstract
The economic disparity between developed and developing countries, and the gap between them, which were and still represent the interest of specialists and change in total agricultural productivity (TFP) is one of the most important measures of comparison to find out this difference. Therefore, the research aims at achieving a set of goals which, in aggregate, constitute an agricultural policy related to measuring the growth in total agricultural productivity (TFP) for both developing and developed countries (Jordan, Saudi Arabia, Canada, Australia) DEAP data Growth in total agricultural productivity in developing countries (Jordan, Saudi Arabia) declined by an average of (0.45 , 0.59)% respectively compared to developed countries (Canada, Australia) which reached (0.82 , 0.88)% respectively. The study has a set of conclusions, the most important being the low capacity Competitiveness in most developing countries and low production and low quality may be due to high costs resulting from high input production prices. Therefore, the study recommends optimal utilization of human, natural and financial resources to increase productivity in agriculture, which is the main input in the development of the agricultural sector of these countries.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".