Bibliographic record
Abstract
Drawing from Claire Mortimer’s historical framing of the romantic comedy, I argue that Hollywood cinema since the 1930s has been developing a genre with plots limited to heteronormative conceptions of love, intimacy, success and sex. My research project Intimate Spaces is a limited series of six episodes with characters created to interrogate the stereotypical nature and predictable flow of intimacies depicted in the mainstream genre. My aim is to destabilize the dominant form by taking up what Lauren Berlant and Michael Warner theorize as ‘normal intimacy’. The script of Intimate Spaces is a reflection on the idea that normative relationships require constant work, what Laura Kipnis notes as a capitalistic reality. Pulling from my personal archive of writing as a queer, BIPOC, first-generation Canadian woman, I alter the romantic comedy genre, making it a site for mutual interpersonal understanding. I present these stories episodically, as a way to center non-conjugal intimate relationships alongside romantic ones, focusing individually on diverse examples of intimate scenes – parent and child, grandparent and grandchild, friends and roommates. These imagined stories were developed and are set in the context of the first year of the COVID-19 pandemic in Toronto. They depict how being jolted into this time of isolation has altered ordinary life differently for each of us, placing new emphasis on our intimate experiences.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.007 |
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".