The Role of Global High-Skilled Talent in Entrepreneurship and Innovation
Bibliographic record
Abstract
This symposium seeks to provide an understanding of the pivotal role of global talent in innovation and entrepreneurship. Against the backdrop of two prominent trends - namely, a significant shift in the high-skilled talent pool from developed countries, particularly the US, to emerging market giants like India and China, alongside the rise of global conflicts (e.g., US-China tensions and the Ukraine conflict)—the role of global talent on innovation and entrepreneurship have encountered increased complexities. This symposium aims to unravel the consequences of these trends and elucidate how firms can capitalize on the evolving global talent distribution. To attain these objectives, the symposium invites four papers on diverse aspects. Two papers will discuss the potential ramifications of global conflicts on knowledge creation and entrepreneurship, while the remaining two will study how firms strategically respond to and capitalize on the availability of global talent. By incorporating studies on both the countries sending talent and those receiving it, as well as investigating various outcomes like entrepreneurship, knowledge production, and firm innovation, this symposium seeks to enrich discussions and enrich the audience with valuable insights for their forthcoming research endeavors. Impact of Global Conflicts on Entrepreneurial Team Formation Author: Astrid Marinoni; Georgia Tech Scheller College of Business Building a Wall Around Science: The Effect of US-China Tensions on International Scientific Research Author: Robert Flynn; Boston U. Questrom School of Business Author: Raviv Murciano-Goroff; Boston U. Questrom School of Business Author: Britta Glennon; The Wharton School, U. of Pennsylvania Author: Jiusi Xiao; Claremont Graduate U. Migrants’ Human and Social Capital: Implications for Immigration Policy Author: Solon Moreira; Fox School of Business, Temple U. Author: Deepak Nayak; The Ohio State U. Fisher College of Business Author: Ram Mudambi; Temple U. Global Democratization of Science and Engineering Education: The Private Value of Inventing Overseas Author: Divya Sebastian; Fuqua School of Business, Duke U.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.000 | 0.005 |
| 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".