Discursive Construction of Nanjing City Image in Public Health Emergency
Bibliographic record
Abstract
In this study, the function of “Nanjing Release”, the official new media platform of the Nanjing Municipal Government, is analyzed in the construction of the city image in paroxysmal public emergencies from five aspects (e.g., discourse content, discourse form, discourse subject, stylistic style, and emotional orientation) based on the framework of pragmatic identity and cultural discourse studies. This study suggests that the discourse contents of “Nanjing Release” primarily comprise pandemic notification, pandemic prevention, and control measures, saluting anti-pandemic workers, serving people’s livelihood, government notification and handling, and pandemic-related science knowledge. Moreover, the forms of discourse are classified into single-modal reports and multi-modal reports. The subjects of discourse primarily include government agencies, anti-pandemic workers, new media organizations, public institutions, and virtual characters. The stylistic styles of discourse are divided into deliberative, formal, casual, and serious styles. Furthermore, the affective orientations include neutral reports, positive reports, and negative reports. This study reveals that the government’s WeChat account, “Nanjing Release”, has built an image of a warm, loving, and grateful city fighting against the pandemic in the public health emergency. Afterward, the motivation for the discursive construction of the city image is studied.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".