Charting the Territories of Epistemic Concepts in the Practice of Science: A Text-Mining Approach
Bibliographic record
Abstract
Much attention in philosophy of science has been devoted to explicating highly prized concepts such as explanation, theory, or model, among many others, resulting in a plurality of nuanced philosophical accounts (for example, the deductive-nomological account, the causal account, the unification account, and the mechanistic account of explanation). The rationale for this enterprise is to be found in the central epistemic roles that such concepts are taken to play in science. But do these concepts actually play such significant roles? In this article, we investigate the actual usage of epistemic concepts in the practice of science by analysing terminological occurrence patterns in scientific publications. Narrowing down the study to six major epistemic concepts (theory, model, mechanism, explanation, understanding, and prediction), we use text-mining methods to quantify actual terminological usage and relationships in a corpus of 73,771 full-text scientific articles of the biological and medical sciences (from the BioMed Central database). The resulting terminological cartographies partly validate select philosophical intuitions, but also suggest notable differences between philosophical reconstructions and the actual roles that epistemic concepts appear to be playing in the scientific discourse. We also investigate the incidence of disciplinary context.
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.037 | 0.048 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".