Multiple Regression Analysis and Weighting Model Construction of National Narrative and National Identity in Modern Chinese Literature
Bibliographic record
Abstract
The nation-state is regarded as the basic form of the modern state, but whether the modern state is really a “one nation, one country” type of political community as depicted by the nation-state narrative. This paper explores the influence of national narratives on national identity by constructing an evaluation index system, using a questionnaire survey method, and taking adolescents as the research object. The independent variables of this study are national narrative, including national language, national spirit and national memory, and the dependent variable is national identity, including cognitive tendency, emotional tendency and behavioral tendency. The weights of the indicators and regression results were calculated by AHP-entropy weighting method. The analysis results show that national vocabulary has a great influence on national language, while sense of belonging is the biggest factor affecting emotional tendency, and most of the dimensions of national narrative are positively correlated to national identity with a significant effect. The correlation coefficient between national etiquette and national identity is 0.203, and the correlation coefficient between national history and national identity is 0.254. National history and national etiquette have a significant effect on national identity.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".