Enhancing Marketing Efficiency of the Saudi Dates at the National and International Markets
Bibliographic record
Abstract
<p class="Default">Date sector is a considerate sector worldwide with an estimated trade value equivalent to about 3.72 billion Saudi Riyals (SR) in 2013. Enhancing marketing efficiency of dates becomes imperative to nations that date sector has a special status in their economies and social heritage such that of the Kingdom of Saudi Arabia.</p><p class="Default">This research paper is targeted to estimate the marketing efficiency of dates at different marketing channels qualitatively using a typical five level LIKERT scale and quantitatively using the Two-Stage Data Envelopment Analysis (2s DEA), to estimate the potential economic impact of improving marketing efficiency on the date marketing channels and on the national economy, and to introduce a set of policies and mechanisms that enhance the competitiveness of the Saudi dates at the local and international markets.</p>The estimated results showed that the total market value of the Saudi dates is about 22.65 billion SR annually, and there is a great potential to improve date marketing efficiency to achieve an additional 30 per cent of value added to traders and the national economy, equivalent to about 6.88 billion SR annually. The research paper concluded with a set of policies and mechanisms to enhance the marketing efficiency and the competitiveness of the Saudi dates at the local and international markets.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".