The model-based view of science: an encouragement to interdisciplinary work
Bibliographic record
Abstract
Widely thought to have made social science more scientific, logical empiricism has tended rather to hamper its balanced development. Its emphasis on the ‘truth’ of ‘scientific laws’ has promoted competition within and among disciplines, and to the rejection and neglect of valuable scientific ideas. The ‘semantic’ or ‘model-based’ school of philosophy of science provides a convincing alternative to logical empiricism. A leading exponent of this school is Ronald Giere in Science Without Laws. For Giere it is the model not the law that is the central element of scientific knowledge. Models are also ‘true’ only in the sense that definitions are true; they are not empirically true. Models are to be judged, not in terms of truth, but in terms of whether they fit some real-world system closely enough for a given purpose. One can have, says Giere, ‘realism without truth’. There can be more than one realistic model pertaining to a given real-world system. Several examples from demography illustrate the negative influence of logical empiricism on cumulative theory and on openness to models from other disciplines. The model-based view, by contrast, encourages a ‘tool kit’ approach to theory and models. All reasonable models are carefully developed and kept ready at hand for use as appropriate. Some may be useful for explanation, others for prediction, still others for policy formation or for teaching. The same spirit leads to openness across disciplines. One values the leading theories and principles of one's own discipline, but one also recognises their inherent limits, as abstract representations of concrete reality. There follows greater appreciation of the tools of other disciplines.
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 | 0.072 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.008 | 0.076 |
| Scholarly communication | 0.026 | 0.040 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.017 | 0.042 |
| Insufficient payload (model declined to judge) | 0.003 | 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".