Bibliographic record
Abstract
Founded in 1995, JDB changed its product brand in 2012 because of a dispute with GPHL, the owner of the Wong Lo Kat brand. GPHL had used the courts to take back control of the Wong Lo Kat brand. From 2012 to 2014, JDB grew continuously in the market but suffered one defeat after another in its lawsuit with GPHL. In 2012, JDB’s ability to use the Wong Lo Kat brand was revoked; in 2013, JDB was stopped by a court ruling from using promotions claiming “Wong Lo Kat has been renamed JDB” and “the ‘red-can herbal tea’ national leader by sales volume has been renamed JDB”; and in December 2014, JDB was stopped by a court ruling from producing and selling the red-can products. With regard to the recent ruling, JDB had bought time between the first trial to the final trial by lodging an appeal, allowing JDB to continue the production and sale of red-can JDB herbal tea. But how long could this strategy work? During this period, how would JDB deal with the ruling on the product? Could the brand transformation implemented over the course of two years be completed in time? During this two-year brand transformation, JDB had won attention by making full use of “The Voice of China” and other major campaigns, but would such campaigns be effective enough to support the future of JDB? Furthermore, could JDB’s campaigns match those of GPHL’s Wong Lo Kat?
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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".