Mike O'Callaghan, American Police Force
Bibliographic record
Abstract
[profile bio] Michael O'Callaghan was the only boy in a family of five children. Growing up in Arizona, Mike became an avid distance runner, competing at a collegiate and national level. When his draft notice came in December of 1972 for the Vietnam War, he reported for his physical evaluation, only to be turned away for inadequate health standards. Three weeks later, he ran the best marathon of his life. Health, for the military, is relative. Mike's parents were relieved, and his mother offered him a bus, plane, or train ticket to Canada before he was to re-report the next year. Fortunately for Mike and his family, the war ended before he could report to the draft board again. Mike stills lives in Arizona and is married to Jeni O'Callaghan. Together, they have three children. Mike works as a middle school math teacher, and is still fully involved in coaching track and field and cross country. He is still running marathons. [profiler bio] Elsa O'Callaghan is the daughter of Mike O'Callaghan and graduate of the University of Southern California. The second profiler, Geoff Parkhill, is a Computer Science Major and has one year left at the University of Southern California.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.138 | 0.242 |
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".