Failed dreams, fresh beginnings: A conversation with Jason Karman on <i>Golden Delicious</i>
Bibliographic record
Abstract
Growing up is never easy, as Asian-Canadian director Jason Karman makes clear in his first feature Golden Delicious (2022). In this interview, we discuss his widely acclaimed film, which was recognized by festivals including the Toronto Reelworld Film Festival, the Vancouver Asian Film Festival, FilmOut San Diego, and the Frameline San Francisco International LGBTQ Film Festival. The film’s title references a restaurant that links three generations of the Wong family. For over 20 years, George (Ryan Mah) and Andrea (Leeah Wong) have worked tirelessly to keep afloat the restaurant that he inherited from his parents. Now, they want bigger and better things for their children—seventeen-year-old Jake (Cardi Wong) and his sister Janet (Claudia Kai)—all the while overlooking their needs and wants. Such tensions are given renewed urgency by the entrance of the Wongs’ attractive new neighbour Aleks (Chris Carson) and by Andrea’s discovery of George’s affair. In this interview, Karman and I discuss some of the challenges of making this film; his reading of its central antagonist Ronald (Jesse Hyde); and the untidiness of its ending. This interview offers insights into intergenerational relationships and investigates how effective cinema can be for exploring their complexities.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.017 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".