Grindr? it’s a “Blackmailer’s goldmine”! The weaponization of queer data publics Amid the US–China trade conflict
Bibliographic record
Abstract
In March 2019, the Committee on Foreign Investment in the United States (CFIUS) identified Grindr, a hookup app that predominantly caters to men who have sex with men, as a “national security threat” and compelled the Chinese conglomerate Kunlun Tech to divest from it entirely. The CFIUS-Grindr ruling is indicative of larger regulatory debates over increasing datafication trends in the dating app industry. Through a political economy approach to communication, this paper examines how this ruling was predominantly constructed by various stakeholders as a public controversy in light of the ongoing US–China trade conflict. This interpretation of the controversy relies on a prejudicial trope that construes queer dating app users as vulnerable targets of potential blackmail schemes operated by Chinese intelligence agencies. Through the Lavender Scare, a historical period referring to state-led investigations into the presence of LGBTQ+ employees in Western federal workforces, this paper historicizes this blackmail trope to highlight how the politicization of queer vulnerabilities amid global hegemonic conflicts is a tactic that predates the US-China trade conflict. It argues that the CFIUS-Grindr ruling weaponizes Grindr’s queer data publics as threats against which the US government should protect itself, while failing to fully recognize the urgency for the state to protect the data privacy rights of the LGBTQ+ communities in the digital economy. In light of the CFIUS-Grindr ruling, this paper examines the implications that datafication raises for the LGBTQ+ communities whose sexual lives and identities are increasingly being datafied and exploited by digital media platforms.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.029 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".