Body part measures in Mandarin Chinese
Bibliographic record
Abstract
This thesis mainly discusses a lexical category known by the name “classifier” or “measure” in Mandarin Chinese. According to Chinese descriptive grammars, which are followed in this thesis, there are at least two types of classifiers, nominal ones (classifiers for nouns) and verbal/ adverbial ones (classifiers for verbs). Within the category of classifiers, there is a subclass that is made of body part terms. Some of them are used for nouns (i.e., NCLs), some of them are used for verbs (i.e., VCLs), and many for both. The correlations between NCLs and VCLs are discussed in this thesis, especially the idea of how both types of classifier can provide delimitation to events. The semantic properties of VCLs and NCLs are explored in this thesis. The [Num+ CL+ N] construction is considered to be a NCLP (nominal classifier phrase) in this thesis and the [V+ [Num+CL]] construction is considered to be a VCLP. I will discuss VCLPs with respect to transitivity of verbs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".