Bibliographic record
Abstract
In this final empirical chapter, I will analyze the role of import-competitors and import-dependent firms during the EU anti-dumping cases against bicycle imports from China and Vietnam. Both cases took place in the 2004–2005 period. Due to the rapidly increasing influx of Chinese and Vietnamese bicycles into the European market between 2001 and 2004, import-competing firms suspected that companies from both countries were dumping their products on the European market. The association representing the import-competing bicycle firms in the EU, the European Bicycle Manufacturing Association (EBMA), sent two requests to the European Commission in 2004: one for an interim review of the antidumping measures on imports from Chinese bicycles and one for investigation into imports from Vietnamese bicycles. In the case of Chinese bicycle imports, this was already the fourth time the EBMA had filed an anti-dumping complaint (the first one was in 1993), while it was the first ever complaint against Vietnam. As a result of these two anti-dumping proceedings, firms depending on bicycle imports from these two countries — that is, bicycle distributors/retailers, sporting goods retailers, and supermarkets — were confronted with a (potential) loss in income and had to choose between political mobilization and adjustment. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".