Media Securitizer and the Racist “China-Threat” Discourse — Ascension, Peak, and Downfall
Bibliographic record
Abstract
My research analyses media treatment of China’s techno-scientific expertise as a proxy to document the conditions under which the China-Threat discourse gains momentum, peak, and subsequently retreats from the field of power. I use the concept of media securitizer to examine the active role that print media’s reporting plays in the construction of China as a security threat in the political field and the public arena. I use qualitative content analysis of 250 news stories from The New York Times published between 1996 and 2016. My findings show the occurrence of two episodes of China-Threat cycles. The first peaked between 1999 and 2001 following Dr. Wen Ho Lee’s nuclear espionage case, while the second cycle gained momentum in 2010 after Beijing perpetrated a cyber-attack against Google. The first episode reveals that The Times played a key securitizing part in the strengthening of the China-Threat views in the political field with their publication of Dr. Lee’ case in 1999 and the specific discourse mobilized to tell the story. The second episode shows, on the contrary, a de-securitizing approach critical of Washington’s and understanding of Beijing’s intentions. Finally, contrary to agenda-setting scholars who argue that the shifting nature of media attention from one issue to another can explain the “end of cycles,” my results indicate the effect of internal forces as well. In both cases, the declining phase of China-Threat cycles was triggered by the unintentional racist effects of over-securitizing a state’s adversary. Dropped prosecutions against American citizens with Chinese heritage accused of acting as Beijing’s spies showcased how sinophobic sentiments participate in the China-Threat downfall. These events operate as “reflexive trigger” that expose the fear-inducing, alarmist, and paranoid nature of the China-Threat.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".