Immigration and Firm Strategy in a New Era
Bibliographic record
Abstract
This symposium aims to bring together pioneering studies that delve into the intricate relationship between immigration and firm strategy, taking into account the evolving socio-political dynamics and delivering valuable insights to inform firm strategic decisions. In recent years, the intersection of immigration and firm strategy has been a subject of growing interest and extensive research. Existing literature has contributed significantly to our understanding of the profound impacts of immigration on knowledge transfer (e.g., Wang, 2015; Yang, Mudambi & Meyer, 2008), entrepreneurship (e.g., Kulchina, 2016, 2017; Lee & Eesley, 2018), and firm strategy and performance (e.g., Hernandez, 2014; Kulchina & Hernandez, 2016; Glennon et al., 2022; Li, Hernandez & Gwon, 2019). However, the current global landscape is witnessing dramatic changes in the socio-political environment for migration and mobility of talents across borders. Shifting demographics in the labor market, resurgence of protectionism (e.g., Yue, et al., 2022), and changing immigration policies, among other factors, have introduced new challenges for firms seeking to harness the potential of talents from around the world. Given these transformative developments, there is an urgent need for further investigation into how businesses can craft effective strategies in this new era. Bridging Ideas: the Role of Migrants in Shaping Entrepreneurial Ventures Author: Astrid Marinoni; Georgia Tech Scheller College of Business The Effect of Immigration Policy on Founding Location Choice: Evidence from Canada's Start-up Visa P Author: Britta Glennon; The Wharton School, U. of Pennsylvania Immigrants and the Assignment of Expatriate Managers: Evidence from South Korean Multinational Compa Author: Elena Kulchina; North Carolina State U.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".