Andrew DiRosa, MPA ‘02, Section Chief (Acting) of the Executive Intelligence Section of the Federal Bureau of Investigation
Bibliographic record
Abstract
Mr. DiRosa obtained a Master’s in Public Administration (MPA), with a focus on managing federal agencies, from The George Washington University in 2002. While at GWU, Andy served as editor of Policy Perspectives, and was awarded the Leadership Award and was inducted into the Pi Alpha Alpha public administration honor society. Andy obtained a BA from Old Dominion University (Norfolk, VA) in English and Political Science in 1987. While at ODU he was elected to two terms on the student senate.Presently, Andy is the assistant section chief of the Executive Intelligence Section in the FBI’s Directorate of Intelligence at FBI Headquarters. In this capacity he helps oversee daily operation of 24-hour units that prepare the daily intelligence briefing materials for the FBI Director, US Attorney General, and other senior executives. Andy has also worked in the FBI’s Counterterrorism Division, as an intelligence analyst and supervisor, and in the FBI’s training division and office of public affairs, as managing editor of the FBI Law Enforcement Bulletin, a widely read criminal justice journal. He authored book reviews and journal articles, including features on street gangs and the impact of the Second World War on US law enforcement. While in the Counterterrorism Division Andy served in an international intelligence cell at NATO headquarters in Brussels. Mr. DiRosa has also represented the FBI at bilateral intelligence exchanges with Canada and the United Kingdom. (Views expressed are those of Mr. DiRosa and do not necessarily reflect the views of the FBI.)
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.086 | 0.040 |
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".