Harley-Davidson: Internationalization in the Trump Era
Bibliographic record
Abstract
“We’re proud of you! Made in America: Harley-Davidson,” President Donald J. Trump praised the motorcycle company’s executives and union leaders on February 2, 2017. When he left the meeting at the White House that day, Matthew S. Levatich, the chief executive officer (CEO) of Harley-Davidson Inc. (Harley-Davidson), was impressed by the Trump administration. The meeting had occurred less than two weeks after Trump had released his America First foreign policy, which could help or hurt the struggling but iconic company that had become a symbol of American ideals and U.S. manufacturing know-how. “The big opportunity for Harley-Davidson, growth-wise, is in Asia, and a lot of the work with the Trans-Pacific Partnership addresses some of the barriers that are in the way of our growth in Asia,” Levatich had said in a television appearance in April 2016.[iii] However, many things had changed since then, and in May 2017, Levatich had to ascertain whether to pursue that big Asian opportunity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".