Political Pressure: An Examination of U.S. Senators’ Actions in Restricting Canadian Softwood Lumber Imports
Bibliographic record
Abstract
Over the past 30 years the U.S.–Canadian softwood lumber trade dispute has resulted in three managed trade agreements that have not been voted on in the U.S. Congress. Nevertheless, U.S. Senators have played an important role in shaping the political environment that has nurtured these agreements. In this paper we construct a lumber influence index based on 14 known events between 2001 and 2006 and analyze what factors influenced a senator's decision to publically call for restricting Canadian lumber imports and to adopt the 2006 Softwood Lumber Agreement. Our results show that the size of the wood products manufacturing industry in a state, campaign contributions, logrolling, and ideology played a significant role and that interest group politics is prevalent in this dispute. Au cours des 30 dernières années, le différend commercial entre le Canada et les États‐Unis au sujet du bois d’œuvre résineux s’est soldé par trois accords de commerce administré qui n’ont pas été mis au vote du Congrès des États‐Unis. Néanmoins, les sénateurs américains ont joué un rôle important dans le façonnement du climat politique dans lequel ces accords ont été préparés. Dans le présent article, nous avons mis au point un indice de l’influence fondé sur 14 événements connus qui se sont déroulés entre 2001 et 2006, et nous avons analysé les facteurs qui ont influencé un sénateur à préconiser publiquement des restrictions sur les importations de bois d’œuvre canadien et à adopter l’Accord sur le bois d’œuvre résineux en 2006. Les résultats de notre étude montrent que la taille de l’industrie de la fabrication des produits en bois dans un État, les contributions aux campagnes, les alliances politiques dans un but intéressé et l’idéologie ont joué un rôle considérable et que l’influence des groupes d’intérêt a été un facteur apparent dans ce différend.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".