Bibliographic record
Abstract
Taiwanese baseball has just been giving a powerful shot in the arm, with three consecutive shots out of the park by the Brother Elephants’ star player Peng Chen-min 彭政閔 during his team’s 7-1 victory over the LaNew Bears in the first game of their playoff series. For video highlights, please click here. I especially like the announcer’s comments about a home run ball resembling a girlfriend who has just had a change of heart — neither is coming back (In the case of one of Taiwan’s 40-something superstars, Chang Tai-shan 張泰山, this is modified to include a reference to his receding hairline). You might also note the Bears fans eating a certain yellow-skinned fruit (a reference to the color of the Elephants’ uniform), which gives new meaning to that old favorite “Yes, We have no Bananas Today”. For an analysis of what this means for Taiwanese baseball, please click here. It is especially noteworthy that over 8,000 fans attended (the largest playoff crowd ever), and that Sunday’s game at Tien-mu’s 10,500-seat stadium is sold out. There is always hope. Finally, for all of you China Beat sports fans who might be wondering what happens when a 102-kilogram Canadian base runner collides with a 88-kilogram Taiwanese catcher at the plate, check this action out.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.694 | 0.487 |
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".