Bibliographic record
Abstract
<JATS1:p>From the show's modest beginnings to its massive Emmy sweep, You Are My Happy Ending tells the story of how Schitt’s Creek became the surprise hit that changed the way we think about LGBTQ relationships.</JATS1:p> <JATS1:p>Cultural analyst Emily Garside shows how this series fused classic romcom and sitcom tropes to create a world with a queer love story at its core, starting with Daniel Levy, the co-creator who plays David. She examines the show’s Canadian identity and its diverse incorporation of references from literature (Brideshead Revisited) to cinema (Hitchcock’s The Birds), as well as numerous romantic comedy texts. Schitt’s Creek is an homage to all these elements of the past literary and cinematic canon while also creating an important contemporary narrative of its own. Most importantly, Garside delves into the references to queer icons and culture—from Cabaret to drag.</JATS1:p> <JATS1:p>How did this supposedly light comedy embrace an activist perspective? And how does it use (and subvert) its romantic-comedy genre in order to make that activism even more powerful? Combining a fan's affection with a scholar's insight, Garside explains how this “little show that could” is the product of a long history of queer activism, breaking down barriers and marking a turning point in future representation of LGBTQ stories.</JATS1:p>
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.105 | 0.016 |
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; both teacher heads agree on what is shown here.
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".