Bibliographic record
Abstract
The approach to the philosophy of science currently known as “Metatheoretical (or Metascientific) Structuralism” (henceforth “MS”, for short), originated in the pioneering work of the late Joseph D. Sneed, The Logical Structure of Mathematical Physics, a bit more than 50 years ago. After an initial phase of gradual consolidation and refinements, to which, besides Sneed himself, his closest collaborators Wolfgang Balzer, C. Ulises Moulines and Wolfgang Stegmüller essentially contributed in the 1970’s and 1980’s, followed soon by other authors mainly in Germany, this metascientific program reached its full maturity in 1987 with the treatise An Architectonic for Science – The Structuralist Program, co-authored by Balzer, Moulines, and Sneed. Since then, a great number of philosophers of science from many different countries around the world, from Australia and China through Canada and the USA to Western Europe, have discussed, refined, and/or further applied MS, both to the foundations of empirical science in general and to the detailed study of particular theories, ranging from classic.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".