Bibliographic record
Abstract
This paper argues that linguistic hierarchies are not real in any linguistic sense but are summaries of linguistic observation or typology. Position in any hi-erarchy is based on intrinsic properties, specjically complexity of linguistic sub-stance. To illustrate, an example involving a change in degree of ergativity across dialects of Inuktitut is discussed. A hierarchy account would only record changes in the use of case, but would not be able to probe the subtle changes in case struc-ture which are suggested by the facts. Under this view, case is not a position on a hierarchy but a syntactic construct, where different cases may have differing com-plexities (Bejar and Hall 1999). [n particular, accusative case has only a little k (or functional case), and cannot license an NP on its own, i.e., is structural. [n contrast, a structure with a little k and a minimum lexical complement can li-cense an NP, i.e., is oblique. The subtle interplay between accusative, partitive and instrumental case in different dialects is examined. It is argued that the Inuk-titut case marker MIK originates as an oblique case in western dialects, but has undergone grammaticalization in eastern dialects. Grammaticalization is seen here to be structure reduction.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".