Bibliographic record
Abstract
SPEAKER: Kasha Patel Deputy Weather Editor The Washington Post REPORTER: Peter J Olson JAMA Network Science editors are a lot like cats: they spend most of their time on computer keyboards and only annoy writers in the process. [Insert laughter here.] That one-liner may or may not strike you as funny—but regardless of whether it made you giggle or groan, I couldn’t resist employing one of the comedy tactics suggested by Kasha Patel during her Plenary Address at the CSE 2023 Annual Meeting in Toronto, particularly given her assertion that just about anyone can craft a joke if they really put their mind to it. A science journalist by day and comedian by night, Patel kicked things off with a lively, rib-tickling routine that focused on her formative years as a self-described nerd—including naming her phone charger “Mitochondria” (because it’s the powerhouse of her cell) and taking on a dubious position in the world of sports (as treasurer of her ultimate frisbee team)—and highlighted a previous and pivotal stint at a “small science startup called NASA.” The latter experience yielded a wellspring of content for her burgeoning career as a stand-up comic; beyond that, it would inspire an extensive empirical endeavor that would help her assess the connections between comedy and science and explore the use of humor as a tool for effective communication of scientific principles. A chemistry major in college, Patel enrolled in a master’s program for science journalism at Boston University while preparing for a run […]
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.602 | 0.659 |
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".