Bibliographic record
Abstract
A major difference between Indo-European Languages and Chinese is that Chinese verbs do not conjugate. This does not mean however that there is no temporal reference in the language. In this paper, a text from a Chinese newspaper is analyzed to determine the role and importance of the various indicators of past time: 1.The context. 2. Certain verbs which, by their very nature, imply past time. 3. Post-verbal suffixes. 4. Adverbs ceng2 or ceng2jing1, and yi3. 5. Specific time and date words. It was found that there was overlap between the various categories and instances where several indicators were used in combination. With reference to category three, le and guo are considered to be aspect rather than tense markers, le being a completed action marker, and guo indicating an action which has already been experienced. However, they generally do refer to past actions or events. In order of frequency, the most common specific markers were adverbs and time and date words, and the least frequent were the post-verbal suffixes. Keywords: Chinese, past time reference RESUMEUne difference majeure entre les langues indo-europeennes et chinoises est que les verbes chinois ne se conjuguent pas. Cela ne signifie pas pour autant qu'il n'y ait pas de reference temporelle dans la langue. Dans cet article, un texte tire d'un journal chinois est analyse pour determiner le role et l'importance des differents indicateurs du temps passe: 1. Le contexte. 2. Certains verbes qui, par leur nature meme, impliquent le temps passe. 3. Des suffixes postverbaux . 4. Les adverbes ceng2 ou ceng2jing1 et yi3. 5. Des references precises a la date et a l'heure. Il a ete constate qu'il y avait un chevauchement entre les differentes categories aussi bien que des cas ou plusieurs indicateurs ont ete utilises en combinaison. En ce qui concerne la categorie trois, le et guo sont consideres comme des marqueurs d’aspect plutot que de temps, le indiquant une action achevee, et guo une action qu’on a deja vecue. Cependant, les deux suffixes indiquent generalement des actions ou des evenements qui ont eu lieu dans le passe. Par ordre de frequence, les marqueurs les plus nombreux etaient les adverbes et les references precises a la date et a l’heure, et les moins frequents etaient les suffixes postverbaux. Mots-cles : chinois, reference au temps pas
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".