A Cognitive Perspective of the Chinese and English Expressions for the Concept of “Present”
Bibliographic record
Abstract
Human beings are the only species that can perceive the existence of time. However, the linguistic expressions for the concept of time are usually indirect. From a cognitive linguistic point of view, the concept of time is materialized via the TIME-AS-SPACE metaphor. However, each culture has its own conceptualization of time, and thus there are various cognitive models for a particular time concept. This paper tries to identify and analyze the Chinese and English expressions for the concept of “present”, so as to establish the cognitive models for this concept in Chinese culture and in English culture respectively. Key words: cognitive model, time concept, present Resume: L’etre humain est la seule espece qui peut percevoir l’existence de temps. Cependant, pour le concept de temps, les expressions linguistiques sont normalement indirectes. Du point de vue linguistique cognitive, le concept de temps est materialise par la metaphore TEMPS-COMME-ESPACE. Cependant, chaque culture a sa propre conceptualisation de temps, donc il y a differents modeles cognitifs pour un particulaire concept de temps. Cette memoire essaie d’identifier et analyser les expressions chinoise et anglaise pour le concept de present, afin d’etablir les modeles cognitifs pour ce concept dans la culture chinoise et dans la culture anglaise respectivement. Mots-Cles: modele cognitif, concept de temps, present
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".