https://researchopenworld.com/discovering-features-of-a-beverage-to-increase-product-use-pakistan-mind-genomics-and-mango-nectar/#
Bibliographic record
Abstract
We present the results of a case history experiment for the introduction of a traditional product, mango nectar, to Pakistan, which has several juice and beverage brands.The objective was to determine whether one could discover the convincing messages for this new product, the brand, and the correct product price, and in turn the product that the mango nectar would replace.The data revealed a clear hierarchy of messages, which were primarily brand and price as the strongest motivators of interest in the mango nectar, and only far below did product features emerge, and below those features emerged other brands and higher prices as the least motivating.A more coherent picture emerged from expected substitution of the nectar for other beverages, with three mind-sets emerging.In order of size these were substitution for juice, for carbonated soft drink, and for lassi, respectively.The segmentation by substitution also revealed that for each substitution mind-set different product features emerged driving interest in the mango nectar.
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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 | 0.006 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.266 | 0.089 |
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".