Bibliographic record
Abstract
Abstract Typologists classify languages as subject‐prominent, topic‐prominent, subject‐prominent and topic‐prominent, and neither subject‐prominent nor topic‐prominent. Some Asian languages that have a large population of speakers, notably Chinese, Japanese, and Korean, are often cited as representatives of topic‐prominent languages. They use topic structures extensively and have a greater variety of them as compared with other languages. A typical topic construction consists of a topic at the left periphery and a comment clause, which is often a full sentence, with or without a gap coreferential with the topic. Where there is no gap, the topic may be semantically or pragmatically related to a certain expression in the comment. The relations are typically those of whole and part, set and member, possessor and possessed, and so on. In some cases, the topic is not related to a particular expression but to the comment as a whole. It is controversial whether gapless topic structures or even gapped ones are formed by movement or by merge, given the fact that island constraints may be violated. Whereas topics are explicitly marked in some topic‐prominent Asian languages like Japanese and Korean, marking is optional in Chinese, so it is debatable whether the sentence‐initial expression is the topic or the subject or other syntactic elements of the sentence. Furthermore, topic also occurs in postsubject and preverbal position in Chinese. Since focus and topic are often not syntactically marked or morphologically distinguishable in Chinese, a nominal expression between the subject and the verb is calledtopicby some linguists andfocusby others. Diagnostic tests are needed to ascertain what it is.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".