A Knowledge Graph-Based Approach to Analyzing the Construction of Urban Cultural Images and Multi-Level Symbolic Flows in Translanguaging Practice
Bibliographic record
Abstract
In this paper, on the basis of relevant theories, based on the adversarial training of BERT-PGD-BiLSTM-CRF entity recognition model and relationship extraction technique to complete the entity extraction and relationship extraction, and then use the entity linking method that fuses attribute and semantic features (BERT+CBOW+CLS) to complete the construction of the knowledge graph and the supplementation of the knowledge graph, and the data is imported into the Neo4j Storage and Display.The symbols contained in the above knowledge graph for the city cultural image in translanguaging practice are divided into three hierarchical symbols, and the symbols are analyzed in terms of flow.In terms of single language usage, English has the highest proportion (22.57%), and Chinese has the best proportion (63.19%) in the process of urban cultural image construction, highlighting the dominant position of Chinese in urban cultural image construction.During the twenty-year period from 2004 to 2023, the trend of social behavioral symbols growth is significantly higher than that of material and spiritual symbol layers, which fits well with the current social development trend.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".