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 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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.038 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".