Linguistic research in the empirical paradigm as outlined by Mario Bunge
Bibliographic record
Abstract
In view of the critique of the methodology of the dominant interdisciplinary research involving language studies as the main component, in particular clinical linguistics, Cummings (Pragmatic disorders. Perspectives in pragmatics, philosophy and psychology, vol 3. Springer, Dordrecht, 2014) proposes that "It is perhaps appropriate at this point to move the debate onto non-empirical grounds." In Cummings (2014: 113) she starts such a debate on the grounds of the philosophy of language and pragmatics. In this article, I propose to expand that debate by including the input of the philosophy of science. I start the discussion by presenting the way one may carry out language research in the paradigm of empirical sciences from the perspective outlined in Bunge (Scientific research. Strategy and philosophy. Berlin, Springer, 1967; Method, model and matter (synthese library). D. Reidel Publishing Company, Dordrecht, 1973; Emergence and convergence: qualitative novelty and the unity of knowledge. University of Toronto Press, Toronto, 2003) and constrained by Altmann's (Towards a theory of language. Glottometrica 1:1-25, 1978) assumption about self-originating and self-regulatory nature of language.
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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 | 0.041 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.006 | 0.085 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".