The culture of numbers: the origins and development of statistics on science
Bibliographic record
Abstract
Measuring science has become an ‘industry’. When, how and why did science come to be measured in the first place? How did a “cultural” activity – science – long reputed to be not amenable to statistics, come to be measured? The statistics owes its existence to the context of the time: 1) measuring the contribution of great men, among them scientists, to civilization, and improving the social conditions of scientists; then 2) science policy and the efficiency of investments in research. Before the 1920s, it was scientists themselves who conducted measurements on science. The statistics collected concerned men of science, or scientists, their demography and geography, their productivity and performance, and were used to promote what was called the advancement of science. In the 1940s and after, the kind of statistics collected changed completely. It was no longer scientists who collected them, but governments and their statistical bureaus. The most cherished statistics was thereafter money devoted to research and development (R&D).
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 | 0.080 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.005 | 0.073 |
| Scholarly communication | 0.018 | 0.031 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".