Bibliographic record
Abstract
Beryl is a leading brand in China's goji berry market that has stayed ahead of the competition quality-wise and is the only one to build a name for itself both offline and online. Beryl made a foray into e-commerce in 2015 and quickly outperformed rivals with low-priced berries. However, its success came at the expense of brand image. In 2018, Beryl repositioned itself by axing lucrative low-price products that brought the company ￥70 million in sales and accounted for 70% of total e-commerce sales. It shifted focus to new goji berry snacks to satisfy the demand for health products among younger generations. In 2020, the Covid-19 outbreak dealt a severe blow to Beryl's offline retail. As a bulwark against the pandemic, Beryl decided to embrace internet retail and make livestream marketing a strategic priority. Beryl cooperated three times with Viya, a top-tier livestreamer on Taobao, quickly boosting its best-selling product—first-crop goji berries. The three Viya livestreams in the first half of 2020 contributed 18% of total sales revenue on Beryl's Tmall flagship store. Nevertheless, given record low livestream prices and factoring in the livestreamer’s commissions and slot fees, Beryl failed to generate profits from them. In terms of goji berry juice and premium Suo Xian berries, the company failed to establish partnerships with top-tier livestreamers. It relied on celebrities and second-tier livestreamers, with a negligible impact on sales. In addition, Beryl organized a series of in-house livestreams but still found it difficult to attract the necessary new customers and convert them into purchasers. The Beryl was faced a conundrum: should the company focus on developing livestream e-commerce to hit its 2020 sales target of ￥130 million? How could it maximize profit from its three major product lines—first-crop berries, berry juice, and Suo Xian berries—through livestreams?
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.139 | 0.066 |
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".