Bibliographic record
Abstract
Purpose The purpose of this article is to provide local authorities, consumers, retailers and vendors information about the consumer profile of shoppers at the organic farmers market that could be used for devising strategies for local organic market development. Design/methodology/approach The survey included information about the socio‐demographic, product characteristics and motives to purchase the product by the organic consumer. The selection of persons to be surveyed was random. The location of the survey was “La Feria del Trueque”, the largest organic farmers market in Costa Rica. During February 2004, 280 surveys were conducted and in 2005, 200 surveys. A single database was developed on the outcomes of the analysis of variance conducted. In order to detect the variables influencing the money spent monthly on organic fresh fruits and vegetables products and rank their importance, an ordinal probit model was specified using a restricted procedure. Findings The organic consumer in Costa Rica appears to be largely of middle age, with high monthly family incomes, female the primary buyer and highly educated. With an average family size of four members, one third of its income is spent on food and between 12 and 20 percent on organic fruits and vegetables and there seems to be a limit of around 20 percent in relation to the premium they are willing to pay for organic products. The organic consumer in Costa Rica recognizing the “obvious” differences seems to be somewhat similar to the organic consumer in the USA, Canada and Europe. Practical implications The similarities detected are significant for those looking to import and sell fresh and processed organic products in Central America, because they can apply international information to open the “local markets”, while they develop their local market research information. Buyers and sellers can use the information of this study to better negotiate local purchasing conditions. At the same time it will help local producers to improve its negotiations capabilities with the new international retailers arriving in Costa Rica. Originality/value The central objectives of this study are to identify the consumer profile of Costa Rica of those buying organic products at the organic farmers markets, and to determine the key variables influencing the consumer expenditures for organic products at the farmers organic 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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".