A Comparative Study on the Cognitive Schema of Spatial Conceptualization of UP-DOWN in Russian and Chinese
Bibliographic record
Abstract
The cognitive schema is one of the topics in cognitive linguistics, and a cognitive pattern formed from the interaction between people and the outside world. Based on Lakoff’s cognitive schema theory, this paper compares the cognitive schema of spatial conceptualization of UP-DOWN in Russian and Chinese, and discusses the cognitive characteristics and meanings of the conceptualization of UP-DOWN by presenting the schemata in both the two languages. It proves that spatial conceptualization, as one of the basic experience of people, influences the structures and meanings of languages. Key words: spatial conceptualization, UP, DOWN, cognitive schema Resume: Le modele cognitif est une problematique de la linguistique cognitive et un moyen de cognition forme sur la base de l’interaction entre l’homme et le monde exterieur. Se referant a la theorie de George Lakoff, l’article present compare les modeles russe et chinois sur la notion spatiale(avec l’exemple de « haut-bas »), examine les caracteristiques cognitives et le sens de la notion « haut-bas » dans les deux langues et les demontrent de facon graphique, pour prouver que la notion spatiale, comme une des experiences fondamentales de l’homme, influe la structure et le sens de la langue. Mots-cles: notion spatiale, « haut », « bas », modele cognitif 摘要：認知模式是認知語言學研究的課題之一，是人與外部世界在互動的基礎上形成的認知方式。本文以萊考夫認知模式的主要理論為依據，比較俄漢語空間概念（以“上——下”為例）的認知模式，討論“上——下”概念在兩種語言中衍生的認知特點和含義並用圖式表示出來，證明了作為人的基本經驗之一的空間概念影響著語言的結構和意義。 關鍵詞：空間概念；“上”；“下”；認知模式
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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 | 0.002 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".