The Semantics of “Black” in Chinese, English, and German: A Colexficational Analysis
Bibliographic record
Abstract
Color term is an abstract concept in human language, which has rich semantic meanings. And people’s understanding of color terms also reflects their ways of thinking. This paper takes the color term “black” as the research object trying to explore the linguistic and cognitive commonality hidden behind the color terms from the perspective of typology. Through the corpora collected from English, German, and Chinese, and combining the colexficational network from CLICS3, the different semantic meanings of the color term “black” are summarized to draw a semantic map. It is found that the semantic meanings of the color term “black” are organized around the two main nodes of “black color” and “darkness”, and then such abstract meanings as “unawareness” and “concealed” are derived. The result shows that the evolution of the semantic meanings of color terms is closely related to human cognition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".