Bibliographic record
Abstract
Abstract The sociological study of science by early pioneers like De Solla Price and Merton has given way to science and technology in society (STS). Latour and Woolgar have studied the social construction of empirical findings in lab work. Sociological studies of Nobel Prize‐winners in science indicate that those who study with Nobel Prize‐winners are themselves the most likely recipients of the Nobel Prize, presumably because they have firsthand information about the cutting‐edge topics and techniques. In hermeneutics the idea of a “hermeneutic circle” or “spiral” in science is associated with “interaction between agents” and close ties between theorists and empirical researchers. In semiotics the notion of an interpretive community or network has been postulated as an aspect of C.S. Peirce's more abstract notion of a recursive “interpretant.” Collins has stressed the general importance of networks. His theory holds that there is a “law of small numbers” and that the half a dozen or so major “philosophers” in any particular time and place are very likely to know one another. Each thinker searches for a niche. Full comprehension of the theory requires an understanding of the network.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 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".