Counter-Examples in Linguistics (Science): The Case of Circassian as a Split Anaphor Language
Bibliographic record
Abstract
Linguists often resist data that undermines the dominant paradigm to which they adhere. This paper examines split anaphors in Circassian, a language of the Caucasus, as a case study of such rejection. A typology of counterexamples is devised and contrastively applied to physics and to linguistics, with etTortsmade to cite examples from each field. The split anaphor case is presented as an error in prediction and hence as a refutation of the Government and Binding paradigm. Its treatment is contrasted with that of the orbit of Mercury, a comparable error in prediction of Newto-nian mechanics. A symmetry-breaking approach is taken to the problem of split anaphor (in which reflexivesare ergativewhile reciprocals are anti-ergative).A new expla-nation for ergativity is offered. This explanation predicts that only ergative languages with a particular rule coupling will exhibit split ergativity. 1.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".