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Record W6985056840

Intimate Spaces

2021· dissertation· en· W6985056840 on OpenAlexaffabout

Bibliographic record

VenueOCAD University Open Research Repository (OCAD University) · 2021
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsRomanceHollywoodNormativeMovie theaterFraming (construction)MainstreamComedyContext (archaeology)
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.004

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.

Opus teacher head0.091
GPT teacher head0.275
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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