Changes in Government Attention to AI Topics in the Perspective of Framing Theory—Taking the Report of AI-related Articles in People's Daily Online as an Example
Bibliographic record
Abstract
This study explores changes in government attention to the topic of AI from a framing theory perspective, using AI-related reports from China People's Daily Online as an example. The study collected about 16,343 articles including from March 2021 to February 2024, and explored the changes in the framing of the reports in the two periods through a combination of qualitative and quantitative research methods, and found that during the period of March 2021 to November 2022, the reports mainly focused on the application and development of AI technology in various fields, presenting a positive propaganda attitude; whereas during the period of December 2022 to February 2024 period, the reports gradually shifted to reflections and critiques of potential risks of AI, showing more diversity and depth. Overall, the government's attitude toward AI technology shows rationality and balance, and in the future, it can continue to pay attention to its role in national security, social governance, and economic development, promote the healthy development of AI technology, and strengthen cooperation with industry, academia, and all sectors of society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| 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 | 0.001 |
| 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".