Bibliographic record
Abstract
The largest share of my gratitude will always be with my wife. Being a spouse of a scientist is just as difficult as being a scientist yourself and Cori managed to perfect this art form flawlessly. If it hadn’t been for her, the Caltech experience would have made me a stranger and more maladjusted person (and I guess that’s saying something). When I sit and think about the effect my advisor, Tom McGill, has had on me, I come up with two things. First, the best and worst thing he has ever done for me as a student is convincing me that I am doing good work and that I am a legitimate researcher. I don’t know if it’s true, but having Tom’s endorsement is the next best thing to believing it myself. Second, Tom runs a group in which we have to learn to do everything. It seems like a pain, but after five years, you find you’ve received an extremely broad education in performing research. Ibenefited in very real ways from interactions with all of my contemporaries in the group. Firstly I need to thank Tim Harris for insulating all of us from the financial side of science and letting us just worry about research. Bob Beach is the only guy in
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 | 0.004 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.530 | 0.370 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".