Bibliographic record
Abstract
FBI Executive Assistant Director Michael Mason remembers well assisting in a four month investigation that extended as far as London, Australia, and Canada, resulting in the return of an abducted little girl to her mother. The joy that Michael felt after their reunion is one of the reasons why he long served the FBI as one of its leading officials. Following his IWU commencement, Michael began a career in the U.S. Marines Corps and achieved the rank of captain. He was sworn in as a special agent of the FBI in 1985. His diverse resume includes assignments at the FBI headquarters, Syracuse, N.Y., and Sacramento, CA. In 2003, FBI Director Robert Mueller appointed Michael to lead the Washington field office, where he oversaw approximately one-half of the FBI’s agent and operational resources and dealt with some of the most important and perplexing cases of the time. Despite his numerous responsibilities, Michael still finds time to assist the IWU community. As the commencement speaker in 2006, he stressed the importance of being bold and never abandoning dreams. Michael and his wife Susan enjoy camping and fishing with his family. Michael serves as a member of the Board of Trustees. He has retired from the FBI and now serves as Vice President Chief Security Officer for Verizon Communications.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.267 | 0.158 |
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".