MétaCan
Menu
Back to cohort
Record W1591826745

International PhDs Will Drive Innovation into the Future

2011· article· en· W1591826745 on OpenAlexaboutno aff
Eric V. Larson

Bibliographic record

VenueResearch-Technology Management · 2011
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsnot available
Fundersnot available
KeywordsGlobeChinaScience and engineeringWork (physics)Engineering educationPolitical scienceManagementEngineeringEconomic growthEconomicsEngineering managementEngineering ethicsPsychologyMechanical engineeringLaw
DOInot available

Abstract

fetched live from OpenAlex

Higher education is booming around the globe. In China alone, the number of colleges and universities has more than doubled in the past decade. And it's easy to see the thought process behind the trend, says Maresi Nerad, founder and director of the Center for Research and Innovation in Graduate Education (CIRGE) at the University of Washington in Seattle. kind of thinking that has inspired many countries to invest in higher education starts with 'Where does innovation come from?' From ideas. 'Who produces those ideas?' It's PhDs- specifically PhDs in engineering and the sciences. The United States is losing its advantage in this key indicator. Although the nation still leads in raw numbers of STEM graduate degrees awarded each year, it will likely be surpassed in the next few years by China, whose numbers have rocketed up, from about 3,000 natural sciences and engineering doctoral degrees in 1993 to about 23,000 in 2006, not far off the nearly 30,000 science and engineering PhDs produced that same year in the United States, according to the National Science Foundation's Science and Engineering Indicators 2010 . Overall U.S. numbers have been holding steady, but a growing percentage of PhDs, and particularly science and engineering PhDs, are awarded to international students. In engineering, two-thirds of U.S. PhDs were awarded to foreign nationals in 2006 according to the NSF's Survey of Earned Doctorates, a growing percentage of whom return to their home countries to live, work, and innovate. Of course, some will stay in the country where they are educated-and that's exactly why Canada, Japan, and the United Kingdom have increased their recruiting efforts. The full importance of science and engineering PhDs in spurring innovation and securing U.S. leadership in the decades to come is well outlined in Rising Above the Gathering Storm, Revisited: Rapidly Approaching Category 5 , a 2010 report issued by the National Academy of Sciences as a review of the current status of issues addressed in the 2005 report, Rising Above the Gathering Storm . The new report, which surveys a host of issues affecting U.S. competitiveness, particularly focuses on the education system. The report strongly suggests, alarmingly, that unless the U.S. K-12 education system does a radical turnaround, the only substantial growth market for PhDs in the near term is to be found in international students: getting them here and plugging them into U.S. institutions that develop new and applied knowledge. In that 2010 report, the Gathering Storm writers suggested a number of U.S. policy changes that could build the stream of international PhD students. Some of those proposed changes: The U.S. should include international students in consideration for national funding of PhD fellowships, improve visa processing and provide preferential visas to STEM students, offer a one-year visa extension to STEM PhDs after graduation, provide citizenship preference to graduates, and remove other barriers to keeping foreign students here that have made their way into U.S. immigration law and policy since 9/11. Changes shouldn't stop at the policy level. We also need to make international students feel more welcome. Campuses should become more like global villages, nurturing more interaction between the visiting student and the local population. …

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.091
Threshold uncertainty score0.305

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0040.004
Scholarly communication0.0170.012
Open science0.0010.008
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0910.052

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.

Opus teacher head0.023
GPT teacher head0.284
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations4
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueResearch-Technology ManagementSame topicBiomedical and Engineering EducationFrench-language works237,207