Probability to buy agricultural products from different sales points during COVID-19: An exemplary scenario analysis
Bibliographic record
Abstract
The world has been facing a challenge. We have remembered that our way of living might change due to outer effects in a very short time period. COVID-19 pandemic that entered into our agenda in the first quarter of 2020 had led to many changes in our daily routines. Survival of lives includes not only being alive but also maintenance of daily needs. The most important need of liveliÂhoods is daily nutrition of course. Yet, both our demand and way of meeting this demand have been facing with challenges as well. When accompanied with rising product prices and amount of products purchased, the operation of the sector was motivatAed with online sales opportunities. Departing from this view, it was intended to compare changing marketing tool preferences of individuals for fresh agricultural products. In this regard probabilities of 499 individuals surveyed online to maintain their agricultural purchases on district bazaars, on suÂpermarkets or malls and via online order and delivAery was compared under different scenarios for Turkey. The findings indicated that with rising spendable income and share of income on fresh product expenditures, probability to use online tools had raised during the pandemic process. Besides, there observed clues that tendency for online shopÂping was higher under lower or no loss of income during the pandemic. Under these conditions, the tendency to make agricultural and fresh product purchases and tools and media of marketing seem to be more challenging for educated and relatively young population in the future. © by PSP.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".