How Can a Taxonomy of Stances Help Clarify Classical Debates on Scientific Change?
Bibliographic record
Abstract
In this paper, we demonstrate how a systematic taxonomy of stances can help elucidate two classic debates of the historical turn—the Lakatos–Feyerabend debate concerning theory rejection and the Feyerabend–Kuhn debate about pluralism during normal science. We contend that Kuhn, Feyerabend, and Lakatos were often talking at cross-purposes due to the lack of an agreed upon taxonomy of stances. Specifically, we provide three distinct stances that scientists take towards theories: acceptance of a theory as the best available description of its domain, use of a theory in practical applications, and pursuit (elaboration) of a theory. We argue that in the Lakatos–Feyerabend debate, Lakatos was concerned with acceptance whereas Feyerabend was mainly concerned with pursuit. Additionally, we show how Feyerabend and Kuhn’s debate on the role of pluralism/monism in normal science involved a crucial conflation of all three stances. Finally, we outline a few general lessons concerning the process of scientific change.
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 | 0.075 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.022 | 0.013 |
| Science and technology studies | 0.015 | 0.079 |
| Scholarly communication | 0.026 | 0.067 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 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".