S&T Revitalization: A New Look
Bibliographic record
Abstract
The issues impacting S&T revitalization and the supply of S&Es are complex and do not usually lend themselves to easy or straightforward solutions.Considering that the United States used to lead the world in the percentage of adults with college degrees but has now fallen to 10th place, and that the outlook for America's ability to compete for jobs in the global economy has continued to deteriorate in the last five years, we need to address this issue and reverse it by a sustained investment in education and basic research to keep from slipping further.Today, globalization of the S&E workforce plays a powerful role in the education and movement of S&E professionals worldwide.While it is debatable whether there is a shortage in the U.S., it is clear that the S&E professional of the future will be more migratory, alleviating the shortage if it exists.Educational opportunities, professional opportunities and higher salaries are all key drivers of the S&E workforce.While opportunities abound in developing countries, for now there continues to be a large influx to the U.S. of international students and professionals in S&E fields.However, global access to a quality free education from world-recognized and respected universities is bound to change this, while simultaneously creating even greater numbers of S&Es on the global stage.In a shrinking world where mobility is less of a restriction than ever, and where growing economies are beginning to recognize and address shortcomings in their educational systems, the question of engineers becoming a global commodity appears more a matter of 'when' rather than 'if'.The revitalization of S&T was examined six years ago in a CECD book entitled From Science to Seapower: A Roadmap for S&T Revitalization, in which ten recommendations were made.The emphasis in this study was primarily on S&T policy issues affecting the U.S. Navy viii Preface and Acknowledgements Labs.These recommendations were revisited in a subsequent edition, Postscript 2010.The fact remains that the total number of students graduating with a bachelor's degree in engineering in the United States continues to drop as a percentage of the total number of bachelor's degrees awarded.With this in mind, we propose to re-examine the issue of workforce revitalization and to focus, explicitly, on the supply of engineers as it is affected by culture, immigration, demographics, and globalization.Our primary purpose in writing this book is to generate a discussion at the national level regarding how best the U.S. can ensure the vitality of the engineering workforce in the coming century.We feel strongly that our engineers need to be well prepared technically, be connected in a meaningful way to the global science and engineering world, and be gender and ethnically diverse and bilingual in order to enhance connectivity with the global S&E community both technically and culturally.The authors wish to acknowledge input from Jim Short on export controls, copy editing done by Eric Hazell, illustrations by Kunal Sakpal, production work by Ania Picard and research assistance on various topics provided by Kevin Ray and Piyush Jain.This book is a continuation of our work and interest in S&T revitalization with the U.S. Navy in mind.It is not intended to be an original work, but rather a compendium of open literature.Its purpose is to provide timely information in sufficient detail to support the development of appropriate policy decisions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".