A Study on the Word 'Hong' (Red) in the Final 40 Chapters of A Dream of Red Chambers
Bibliographic record
Abstract
This research paper is based on the text edited by Feng Qiyong’s A Collection of Eight Schools of Criticism on Hong Lou Meng( 《 八家評批紅樓夢 》 ) . It was published by Culture and Arts Press, Beijing in September, 1991. The focus areas of this paper: A Study on the Word ‘Hong’ (Red) in the final 40 Chapters of A Dream of Red Chambers are as follows: 1.1 Frequency of occurence: The number of occurences of the word ‘Hong’ (Red) in the text of the final forty chapters is 159. In one of the Chapters,the occurrences of the word ‘Hong’ (Red) is 0 and it is the lowest number of occurrences.In another chapter, the word ‘Hong’ (Red) occurs 20 times, which is the highest frequencies of all the chapters. 0, 5, 7, 20 frequencies of occurences occurs once only in chapter 105, 82, 102 and 88, the least number of the chapters it occurs. There are nine chapters with only once occurrences of the word ‘Hong’ (Red) among the final forty chapters, the highest occurrences of the chapters. 1.2 Morphological formation: There are 22 words formed through the morphological formation based on morpheme ‘Hong’ (Red).Out of the 22 words, 13 are bisyllabic words, 4 are trisyllabic words and 5 are tetrasyllabic words respectively. 1.3 Structural approach: There are 5 structural types of the bisyllabic word namely subject predicate type, modifier type, predicate object type, joint type and embedded type; The two types of trisyllabic word are modified type and embedded type. The three types of tetrasyllabic word are modified type, joint type and embedded type. 1.4 Collocation productivity: There are 33 phrases formed from the word ‘Hong’ (Red) or morpheme ‘Hong’ (Red). They are categorized into 7 syllabic formation patterns and 5 morphological patterns. 1.5 Syntactic functions: There are 19 examples of using the monosyllabic word’Hong’ (Red) as the syntactic components of the 5 sentences. The polysyllabic words constructed from the morpheme ‘Hong’ (Red) are shown in various syntactic functions. 1.6 Lexical expansion: The monosyllabic word “Hong” has four different meanings. The polysyllabic words constructed from the morpheme ‘Hong’ (Red) has 18 single meanings and the other four with two different meanings. 33 phrases are constructed from the the word ‘Hong’ (Red) or the morpheme ‘Hong’ (Red). ‘Hong’ (Red) contains polysemous meaning in 3 phrases, whereas the rest are monosemous. 1.7 Semantic structure: There are 6 meaning categories for the monosyllabic word ‘Hong’ (Red) and the polysyllabic words formed by ‘Hong’ (Red). The six meaning categories can be categorized into 4 levels. 1.8 Cultural connotation: There are four cultural connotations for the word ‘Hong’ (Red) in the text. Key words : A Dream of Red Chambers ; The Final 40 Chapters; The Word ‘Hong’ (Red); The Meanings of the Word ‘Hong’ (Red); Cultural Connotations
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".