Bibliographic record
Abstract
Given the increasing interconnectivity worldwide, the number of Americans finding romantic partners outside their nation is rising. American citizens can bring their partner to the US through the K1 Fiancé visa, which gives the couple 90 days to wed before the foreign partner must leave the country. The widely popular American reality TV show, 90 Day Fiancé, documents binational couples’ journey through the 90 days. This research explores media representations of binational couples in 90 Day Fiancé through critical discourse analysis. Focusing on the production and editing of the show – specifically music, narration, camera shots, and sequencing of footage – intersections of nationality, ethnicity, and sexuality are explored. Ultimately, I argue that the show is shaped by a long history of nationalist and xenophobic discourses, which have ties to the US’s colonial past. The show relies on these narratives to create drama, building and playing on audiences' implicit understanding of these dominant discourses to build a show that asks viewers to decipher the intentions of the foreign partner (i.e. are they in love with the person or in love with America?). This research offers insight into ways that colonialism is reproduced in reality TV.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".