Bibliographic record
Abstract
aving recently attended my fiftieth college reunion, it is appropriate that my expressions of gratitude begin with the Amherst College 1970s physics faculty, who nominated me a half-century ago for membership in Sigma Xi, the scientific honor society.This event (coupled with my payment of annual dues) has brought me, six times a year, The American Scientist magazine.As I note in chapter 10, it was a particularly enchanting article in this magazine that sparked my interest in how we can reconstruct historyand prehistory-using atoms.After a few years and a few more articles in that magazine and elsewhere, I had collected enough material to design a course for nonscience majors at Columbia University; I called it "The Universal Timekeeper."At one point, I had hoped this inherently multidisciplinary course might evolve into the Core Curriculum science course I had been trying to add to Columbia's curriculum for decades.It didn't, but that goal was eventually fulfilled by the course that inspired my last book, A Survival Guide to the Misinformation Age: Scientific Habits of Mind.Nonetheless, I still occasionally teach "The Universal Timekeeper" and must call out for special mention my class in the fall term of 2022 who, with the motivation of bonus points, corrected many errors in the proofs of this book, leaving few, I trust, for the reader to discover.Caroline Nicholson, in particular, was the most perspicacious proofreader by a wide margin.I am also indebted to two anonymous reviewers who suggested that I add a glossary to this book and helped me to clarify several descriptions in the text.A particularly fortuitous and salutary reconnection with a former student of mine from Quest University Canada, Nessa Bryce, and her sister Maggie led to one of the most exciting and fulfilling aspects of completing this book: designing
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".