Firm foreign activity and the geography of exchange rate risk [update of 2022-02]
Bibliographic record
Abstract
67 p. ; Includes bibliographical references (pp. 40-44) ; Acknowledgements: We thank George Allayannis, Joon Woo Bae, Ron Balvers, Yiying Cheng (discussant), Lilian Ng, Liu Sining (discussant), Takeshi Yamada, Le Zhang, and seminar and conference participants at the Southern Finance Association annual meeting, Midwest Finance Association annual meeting, Asian Finance Association annual meeting, International Risk Management Conference meeting and the College of Business and Economics at Australian National University for their helpful comments. Amir Akbari is an Assistant Professor of Finance at the DeGroote School of Business, McMaster University. Francesca Carrieri is an Associate Professor of Finance at the Desautels Faculty of Management, McGill University. We are grateful to the Southern Finance Association for the 2022 best paper award in International Finance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.097 | 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 teacher head, 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".