The Practice and Sociology of Natural Science
Bibliographic record
Abstract
<!--HTML--> Lessons about research in the natural sciences can be drawn from the sociology of science. For example, in 1960 Einstein's general theory of relativity was standard and accepted physics, and elements of it were on the qualifying exam I wrote as a graduate student. But there was little empirical support for this theory; it was what sociologists could rightly term a social construction. That has changed, but now we have other social constructions in cosmology; consider the schematic model for dark matter. I will offer more lessons of this sort drawn from how physical cosmology grew from a social construction to a well-tested empirical construction. Prof. James Peebles was born in St. Boniface, Canada. After attending the University of Manitoba, he continued his studies at Princeton University in the United States, receiving his doctorate there in 1962. He remained at Princeton University where he is now Professor Emeritus of Science. James Peebles’ theoretical framework, developed since the mid-1960s, is the basis of our contemporary ideas about the universe. The cosmic background radiation is a remaining trace of the formation of the universe. Using his theoretical tools and calculations, James Peebles was able to interpret these traces from the infancy of the universe and discover new physical processes. The results showed us a universe in which just five per cent of its content is known matter. The rest, 95 per cent, is unknown dark matter and dark energy. He shared the Nobel Prize in Physics 2019 for his theorical discoveries in physical cosmology.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.089 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".