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Record W7055528888

David Hopkins, Participant

2025· article· en· W7055528888 on OpenAlexaboutno aff

Bibliographic record

VenueScholarly Commons (Embry–Riddle Aeronautical University) · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsOfficerCredibilityVice presidentLeverage (statistics)Corporate governanceHigh techChief executive officerStrategic management
DOInot available

Abstract

fetched live from OpenAlex

David Hopkinsis an innovative leader who guides companies in highly regulated industries taking technology to the “next level” as their businesses expand and grow through merger and acquisition. He is known for transforming vague issues into effective strategies and roadmaps that fully leverage the potential of evolving technology. With a unique ability to make the mundane exciting, he gets others to see the possibilities. David is articulate, persuasive and has a sense of humor that builds credibility and support. Currently David serves as the Chief Information Officer for Mesa Airlines, a regional contract airline headquartered in Phoenix, AZ. David recently secured funding and resources to standup a major cybersecurity program for Mesa which will surpass TSA and newly announced, SEC cybersecurity requirements. Mesa Airlines operates a large fleet of regional jets and narrow body aircraft on behalf of our partners: United Express and DHL Express throughout the US, as well as Canada, Mexico, Cuba and the Bahamas. Previously David was the Vice President of IT Applications for CSAA Insurance Group, a AAA company, where David championed the firm’s technology and digital transformation. Prior to that role, David rose through the ranks of American Airlines to division CIO/Managing Director of IT for Technical Operations (Tech Ops). At American Airlines David spearheaded the creation of the Project Management Office, effectively managing the integration of hundreds of Tech Ops systems through merger with US Airways. Earlier as an IT strategy expert, David championed a business-focused technology roadmap and overhauled the division’s governance structure. David received a BSc in Computer Science from the University of Missouri and participated in the Director Education program of the Raj & Kamla Gupta Governance Institute, Drexel University. He holds two U.S. patents for RFID inventory management technology. Active in professional organizations and in the community, David is also an author, keynote speaker and Licensed Private Pilot.

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)0.004
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.834
Threshold uncertainty score0.556

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0090.003
Scholarly communication0.0090.007
Open science0.0020.007
Research integrity0.0060.012
Insufficient payload (model declined to judge)0.1660.041

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 head0.029
GPT teacher head0.256
Teacher spread0.227 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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