Bibliographic record
Abstract
This research explores the textual description, intertextual interpretation, and social contexts explanation of 13 editorials on Huawei, Meng Wanzhou, and Canada published in The Global Times, Huanqiu Shibao between Dec. 12th, 2018 and Jan. 29th, 2019 with the focused editorial of Dec. 16th titled Let the Country Pursuing the Invasion of China’s Interests Pay the Price. Employing a method established by Fairclough’s Critical Discourse Analysis(CDA), the first findings of the textual description center around the emergence of an allies in the context of U.S.-China Trade War by using 1) different linguistic strategies toward the countries of Five Eyes, 2) contrastive address terms that show mixed signals, 3) a war frame, and 4) an expert voice. Second, intertexuality interpreted from the titles and main ideas of 12 relevant editorials reveals the repetitive linguistic strategy patterns including 1) the ambiguity strategy toward U.S., 2) the double layered linguistic strategies including threatening and conciliating expressions toward Australia, New Zealand, and Canada, and 3) avoidance strategy toward U.K. Third, the findings discuss and explain the underlying social conditions in which the editorials of The Global Times attempt to convince the readers that China needs to crack the US intelligence alliance called Five Eyes so that the Chinese government can succeed to lead the 5th generation network business through Huawei and therefore wish the success of Made in China 2025, a blueprint to upgrade the Chinese manufacturing policy in the pursuit of high value products and services.
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 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".