Bibliographic record
Abstract
Recourse has been taken to Thomas Kuhn’s model of scientific change in order to address the Neogrammarians’ “revolution” in linguistics. But the Kuhnian approach seems inadequate in view of the absence of any “crisis” at the empirical level prior to the Brugmann/Osthoff manifesto, and of any ensuing relation of “incommensurability” between paradigms, granted that a “paradigm shift” did occur. We aim at showing that Imre Lakatos’ “Methodology of Scientific Research Programs” is better suited as analytic framework in the epistemological assessment of the Neogrammarian movement. In particular, Lakatos’ notions of “hard core” and “negative heuristic” of a “scientific research program” provide a better understanding of the impact of the two main tenets of the movement. This view suggests that the Neogrammarians, while aiming with their first principle (the exceptionlessness of sound laws) at strengthening the notion of explanation in linguistic theory, integrated the principle of analogy (which formerly functioned as “negative heuristic”) within the “hard core” of their scientific program.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".