Bibliographic record
Abstract
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 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 teacher head, 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".