Bibliographic record
Abstract
![Graphic][1] Don't worry about J Edgar Hoover not letting you become an FBI agent (women weren't allowed in back then), you'll use your investigative passion in other ways! Watching the Red Sox win the World Series in McGreevy's, the oldest sports bar in the USA, with my husband, brother, sister and 85-year-old Dad—who drank more beers that night than I could believe! This was an especially emotional win as it was the year of the Boston Marathon bombings (my husband and I were in the finish line medical tent that day) and the Red Sox were playing for Boston Strong. McGreevy's is just a couple of blocks from the finish line so everyone migrated there to celebrate after the win. It was a magical night for Boston! Being willing to look at an issue in a completely different way and letting go of my preconceived ideas. We need to appreciate that the scientific truth of 10 years ago may not be the truth today, and the truth today may not be the truth 10 years from now. Honestly, my husband. After an amazing 25-year career in Special Forces, he came out of retirement to … [1]: /embed/inline-graphic-1.gif
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.183 | 0.162 |
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".