Bibliographic record
Abstract
Abstract What kinds of changes in foreign competition lead domestic industries to seek import protection? To address this question, we use detailed monthly US import data to investigate changes in import composition during a 24‐month window immediately preceding the filing of a petition for import protection. A decomposition methodology allows a comparison of imports from two groups of countries supplying the same product: those that are named in the petition and those that are not. The same decomposition can be applied to products quite similar to the imports in question, but not subject to a petition. The results suggest that industries typically seek protection when faced with a specific pattern of shocks. First, a persistent positive relative supply shock favours imports from named countries. Second, a negative demand shock hits imports from all sources just prior to domestic industries’ petition for protection. The relative supply shock is a broad one; it applies both to named commodities and to the comparison product group. The import demand shock, by contrast, is narrow, hitting only named products. This negative import demand shock appears to be a key event in the run‐up to the filing of a petition. This latter shock has been missed by previous studies using more aggregated data.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".